=Paper=
{{Paper
|id=Vol-1766/oaei16_paper0
|storemode=property
|title=Results of the Ontology Alignment Evaluation Initiative 2016
|pdfUrl=https://ceur-ws.org/Vol-1766/oaei16_paper0.pdf
|volume=Vol-1766
|authors=Manel Achichi,Michelle Cheatham,Zlatan Dragisic,Jérôme Euzenat,Daniel Faria,Alfio Ferrara,Giorgos Flouris,Irini Fundulaki,Ian Harrow,Valentina Ivanova,Ernesto Jiménez-Ruiz,Elena Kuss,Patrick Lambrix,Henrik Leopold,Huanyu Li,Christian Meilicke,Stefano Montanelli,Catia Pesquita,Tzanina Saveta,Pavel Shvaiko,Andrea Splendiani,Heiner Stuckenschmidt,Konstantin Todorov,Cássia Trojahn,Ondřej Zamazal
|dblpUrl=https://dblp.org/rec/conf/semweb/AchichiCDEFFFFH16
}}
==Results of the Ontology Alignment Evaluation Initiative 2016==
https://ceur-ws.org/Vol-1766/oaei16_paper0.pdf
Results of the
Ontology Alignment Evaluation Initiative 2016?
Manel Achichi1 , Michelle Cheatham2 , Zlatan Dragisic3 , Jérôme Euzenat4 ,
Daniel Faria5 , Alfio Ferrara6 , Giorgos Flouris7 , Irini Fundulaki7 , Ian Harrow8 ,
Valentina Ivanova3 , Ernesto Jiménez-Ruiz9,10 , Elena Kuss11 , Patrick Lambrix3 ,
Henrik Leopold12 , Huanyu Li3 , Christian Meilicke11 , Stefano Montanelli6 ,
Catia Pesquita13 , Tzanina Saveta7 , Pavel Shvaiko14 , Andrea Splendiani15 , Heiner
Stuckenschmidt11 , Konstantin Todorov1 , Cássia Trojahn16 , and Ondřej Zamazal17
1
LIRMM/University of Montpellier, France
lastname@lirmm.fr
2
Data Semantics (DaSe) Laboratory, Wright State University, USA
michelle.cheatham@wright.edu
3
Linköping University & Swedish e-Science Research Center, Linköping, Sweden
{zlatan.dragisic,valentina.ivanova,patrick.lambrix}@liu.se
4
INRIA & Univ. Grenoble Alpes, Grenoble, France
Jerome.Euzenat@inria.fr
5
Instituto Gulbenkian de Ciência, Lisbon, Portugal
dfaria@igc.gulbenkian.pt
6
Università degli studi di Milano, Italy
{alfio.ferrara,stefano.montanelli}@unimi.it
7
Institute of Computer Science-FORTH, Heraklion, Greece
{jsaveta,fgeo,fundul}@ics.forth.gr
8
Pistoia Alliance Inc., USA
ian.harrow@pistoiaalliance.org
9
Department of Informatics, University of Oslo, Norway
ernestoj@ifi.uio.no
10
Department of Computer Science, University of Oxford, UK
11
University of Mannheim, Germany
{christian,elena,heiner}@informatik.uni-mannheim.de
12
Vrije Universiteit Amsterdam, The Netherlands
h.leopold@vu.nl
13
LASIGE, Faculdade de Ciências, Universidade de Lisboa, Portugal
cpesquita@di.fc.ul.pt
14
TasLab, Informatica Trentina, Trento, Italy
pavel.shvaiko@infotn.it
15
Novartis Institutes for Biomedical Research, Basel, Switzerland
andrea.splendiani@novartis.com
16
IRIT & Université Toulouse II, Toulouse, France
{cassia.trojahn}@irit.fr
17
University of Economics, Prague, Czech Republic
ondrej.zamazal@vse.cz
Abstract. Ontology matching consists of finding correspondences between se-
mantically related entities of two ontologies. OAEI campaigns aim at comparing
?
The official results of the campaign are on the OAEI web site.
ontology matching systems on precisely defined test cases. These test cases can
use ontologies of different nature (from simple thesauri to expressive OWL on-
tologies) and use different modalities, e.g., blind evaluation, open evaluation, or
consensus. OAEI 2016 offered 9 tracks with 22 test cases, and was attended by
21 participants. This paper is an overall presentation of the OAEI 2016 campaign.
1 Introduction
The Ontology Alignment Evaluation Initiative1 (OAEI) is a coordinated international
initiative, which organises the evaluation of an increasing number of ontology matching
systems [18,21]. Its main goal is to compare systems and algorithms openly and on
the same basis, in order to allow anyone to draw conclusions about the best matching
strategies. Furthermore, our ambition is that, from such evaluations, tool developers can
improve their systems.
Two first events were organised in 2004: (i) the Information Interpretation and
Integration Conference (I3CON) held at the NIST Performance Metrics for Intelli-
gent Systems (PerMIS) workshop and (ii) the Ontology Alignment Contest held at
the Evaluation of Ontology-based Tools (EON) workshop of the annual International
Semantic Web Conference (ISWC) [41]. Then, a unique OAEI campaign occurred in
2005 at the workshop on Integrating Ontologies held in conjunction with the Inter-
national Conference on Knowledge Capture (K-Cap) [4]. From 2006 until now, the
OAEI campaigns were held at the Ontology Matching workshop, collocated with ISWC
[19,17,6,14,15,16,2,9,12,8], which this year took place in Kobe, JP2 .
Since 2011, we have been using an environment for automatically processing eval-
uations (§2.2), which has been developed within the SEALS (Semantic Evaluation At
Large Scale) project3 . SEALS provided a software infrastructure, for automatically exe-
cuting evaluations, and evaluation campaigns for typical semantic web tools, including
ontology matching. In the OAEI 2016, all systems were executed under the SEALS
client in all tracks, and evaluated with the SEALS client in all tracks. This year we
welcomed two new tracks: the Disease and Phenotype track, sponsored by the Pistoia
Alliance Ontologies Mapping project, and the Process Model Matching track. Addi-
tionally, the Instance Matching track featured a total of 7 matching tasks based on all
new data sets. On the other hand, the OA4QA track was discontinued this year.
This paper synthesises the 2016 evaluation campaign. The remainder of the paper
is organised as follows: in Section 2, we present the overall evaluation methodology
that has been used; Sections 3-11 discuss the settings and the results of each of the test
cases; Section 12 overviews lessons learned from the campaign; and finally, Section 13
concludes the paper.
2 General methodology
We first present the test cases proposed this year to the OAEI participants (§2.1). Then,
we discuss the resources used by participants to test their systems and the execution
1
http://oaei.ontologymatching.org
2
http://om2016.ontologymatching.org
3
http://www.development.seals-project.eu
environment used for running the tools (§2.2). Finally, we describe the steps of the
OAEI campaign (§2.3-2.5) and report on the general execution of the campaign (§2.6).
2.1 Tracks and test cases
This year’s OAEI campaign consisted of 9 tracks gathering 22 test cases, and different
evaluation modalities:
The benchmark track (§3): Like in previous campaigns, a systematic benchmark se-
ries has been proposed. The goal of this benchmark series is to identify the areas
in which each matching algorithm is strong or weak by systematically altering an
ontology. This year, we generated a new benchmark based on the original biblio-
graphic ontology and another benchmark using a film ontology.
The expressive ontology track offers alignments between real world ontologies ex-
pressed in OWL:
Anatomy (§4): The anatomy test case is about matching the Adult Mouse
Anatomy (2744 classes) and a small fragment of the NCI Thesaurus (3304
classes) describing the human anatomy.
Conference (§5): The goal of the conference test case is to find all correct cor-
respondences within a collection of ontologies describing the domain of or-
ganising conferences. Results were evaluated automatically against reference
alignments and by using logical reasoning techniques.
Large biomedical ontologies (§6): The largebio test case aims at finding align-
ments between large and semantically rich biomedical ontologies such as
FMA, SNOMED-CT, and NCI. The UMLS Metathesaurus has been used as
the basis for reference alignments.
Disease & Phenotype (§7): The disease & phenotype test case aims at finding
alignments between two disease ontologies (DOID and ORDO) as well as be-
tween human (HPO) and mammalian (MP) phenotype ontologies. The evalua-
tion was semi-automatic: consensus alignments were generated based on those
produced by the participating systems, and the unique mappings found by each
system were evaluated manually.
Multilingual
Multifarm (§8): This test case is based on a subset of the Conference data set,
translated into ten different languages (Arabic, Chinese, Czech, Dutch, French,
German, Italian, Portuguese, Russian, and Spanish) and the corresponding
alignments between these ontologies. Results are evaluated against these align-
ments.
Interactive matching
Interactive (§9): This test case offers the possibility to compare different match-
ing tools which can benefit from user interaction. Its goal is to show if user
interaction can improve matching results, which methods are most promising
and how many interactions are necessary. Participating systems are evaluated
on the conference data set using an oracle based on the reference alignment,
which can generate erroneous responses to simulate user errors.
Instance matching (§10). The track aims at evaluating the performance of match-
ing tools when the goal is to detect the degree of similarity between pairs of
test formalism relations confidence modalities language SEALS
√
benchmark OWL = [0 1] blind EN
√
anatomy OWL = [0 1] open EN
√
conference OWL =, <= [0 1] open+blind EN
√
largebio OWL = [0 1] open EN
√
phenotype OWL = [0 1] blind EN
AR, CZ, CN, DE, EN, √
multifarm OWL = [0 1] open+blind
ES, FR, IT, NL, RU, PT
√
interactive OWL =, <= [0 1] open EN
√
instance OWL = [0 1] open(+blind) EN(+IT)
√
process model OWL <= [0 1] open+blind EN
Table 1. Characteristics of the test cases (open evaluation is made with already published refer-
ence alignments and blind evaluation is made by organisers from reference alignments unknown
to the participants).
items/instances expressed in the form of OWL Aboxes. Three independent tasks
are defined:
SABINE: The task is articulated in two sub-tasks called inter-lingual mapping and
data linking. Both sub-tasks are based on OWL ontologies containing topics as
instances of the class “Topic”. In inter-lingual mapping, two ontologies are
given, one containing topics in the English language and one containing topics
in the Italian language. The goal is to discover mappings between English and
Italian topics. In data linking, the goal is to discover the DBpedia entity which
better corresponds to each topic belonging to a source ontology.
SYNTHETIC: The task is articulated in two sub-tasks called UOBM and SPIM-
BENCH. In UOBM, the goal is to recognize when two OWL instances be-
longing to different data sets, i.e., ontologies, describe the same individual. In
SPIMBENCH, the goal is to determine when two OWL instances describe the
same Creative Work. Data Sets are produced by altering a set of original data.
DOREMUS: The DOREMUS task contains real world data coming from the
French National Library (BnF) and the Philharmonie de Paris (PP). Data are
about classical music work and follow the DOREMUS model (one single vo-
cabulary for both datasets). Three sub-tasks are defined called nine hetero-
geneities, four heterogeneities, and false-positive trap characterized by differ-
ent degrees of heterogeneity in work descriptions.
Process Model Matching (§11): The track is concerned with the application of ontol-
ogy matching techniques to the problem of matching process models. It is based
on a data set used in the Process Model Matching Campaign 2015 [3], which has
been converted to an ontological representation. The data set contains nine process
models which represent the application process for a master program of German
universities as well as reference alignments between all pairs of models.
Table 1 summarises the variation in the proposed test cases.
2.2 The SEALS client
Since 2011, tool developers had to implement a simple interface and to wrap their tools
in a predefined way including all required libraries and resources. A tutorial for tool
wrapping was provided to the participants, describing how to wrap a tool and how to
use the SEALS client to run a full evaluation locally. This client is then executed by
the track organisers to run the evaluation. This approach ensures the reproducibility and
comparability of the results of all systems.
2.3 Preparatory phase
Ontologies to be matched and (where applicable) reference alignments have been pro-
vided in advance during the period between June 1st and June 30th , 2016. This gave
potential participants the occasion to send observations, bug corrections, remarks and
other test cases to the organisers. The goal of this preparatory period is to ensure that
the delivered tests make sense to the participants. The final test base was released on
July 15th , 2016. The (open) data sets did not evolve after that.
2.4 Execution phase
During the execution phase, participants used their systems to automatically match the
test case ontologies. In most cases, ontologies are described in OWL-DL and serialised
in the RDF/XML format [11]. Participants can self-evaluate their results either by com-
paring their output with reference alignments or by using the SEALS client to compute
precision and recall. They can tune their systems with respect to the non blind evalua-
tion as long as the rules published on the OAEI web site are satisfied. This phase has
been conducted between July 15th and August 31st , 2016. Unlike previous years, we
requested a mandatory registration of systems and a preliminary evaluation of wrapped
systems by July 31st. This reduced the cost of debugging systems with respect to issues
with the SEALS client during the Evaluation phase as it happened in the past.
2.5 Evaluation phase
Participants were required to submit their wrapped tools by August 31st , 2016. Tools
were then tested by the organisers and minor problems were reported to some tool
developers, who were given the opportunity to fix their tools and resubmit them.
Initial results were provided directly to the participants between September 23rd and
October 15th , 2016. The final results for most tracks were published on the respective
pages of the OAEI website by October 15th , although some tracks were delayed.
The standard evaluation measures are usually precision and recall computed against
the reference alignments. More details on the evaluation are given in the sections for
the test cases.
2.6 Comments on the execution
Following the recent trend, the number of participating systems has remained approx-
imately constant at slightly over 20 (see Figure 1). This year was no exception, as we
counted 21 participating systems (out of 30 registered systems). Remarkably, partic-
ipating systems have changed considerably between editions, and new systems keep
emerging. For example, this year 10 systems had not participated in any of the previ-
ous OAEI campaigns. The list of participants is summarised in Table 2. Note that some
systems were also evaluated with different versions and configurations as requested by
developers (see test case sections for details).
Fig. 1. Number of systems participating in OAEI per year.
3 Benchmark
The goal of the benchmark data set is to provide a stable and detailed picture of each
algorithm. For that purpose, algorithms are run on systematically generated test cases.
3.1 Test data
The systematic benchmark test set is built around a seed ontology and many variations
of it. Variations are artificially generated by discarding and modifying features from a
seed ontology. Considered features are names of entities, comments, the specialisation
hierarchy, instances, properties and classes. This test focuses on the characterisation of
the behaviour of the tools rather than having them compete on real-life problems. Full
description of the systematic benchmark test set can be found on the OAEI web site.
Since OAEI 2011.5, the test sets are generated automatically from different seed
ontologies [20]. This year, we used two ontologies:
biblio The bibliography ontology used in the previous years which concerns biblio-
graphic references and is inspired freely from BibTeX;
film A movie ontology developed in the MELODI team at IRIT (FilmographieV14 ). It
uses fragments in French and labels in French and English.
The characteristics of these ontologies are described in Table 3.
The film data set was not available to participants when they submitted their sys-
tems. The tests were also blind for the organisers since we did not look into them before
running the systems.
The reference alignments are still restricted to named classes and properties and use
the “=” relation with confidence of 1.
4
https://www.irit.fr/recherches/MELODI/ontologies/
FilmographieV1.owl
DKP-AOM-Lite
LogMap-Bio
CroMatcher
DKP-AOM
PhenoMM
LogMapLt
PhenoMP
DiSMatch
PhenoMF
FCA-Map
CroLOM
LogMap
Total=21
Lyam++
LPHOM
RiMOM
SimCat
NAISC
XMap
AML
Alin
Lily
System
√ √ √ √ √ √ √ √ √ √ √ √ √
Confidence 13
√ √ √ √ √ √
benchmarks 6
√ √ √ √ √ √ √ √ √ √ √ √ √
anatomy 13
√ √ √ √ √ √ √ √ √ √ √ √ √
conference 13
√ √ √ √ √ √ √ √ √ √ √ √ √
largebio 13
√ √ √ √ √ √ √ √ √ √ √
phenotype 11
√ √ √ √ √ √ √
multifarm 7
√ √ √ √
interactive 4
√ √ √ √
process model 4
√ √ √ √
instance 4
total 9 1 4 1 4 4 4 4 9 3 6 4 5 1 1 1 1 1 1 7 77
Table 2. Participants and the state of their submissions. Confidence stands for the type of results
returned by a system: it is ticked when the confidence is a non-boolean value.
Test set biblio film
classes+prop 33+64 117+120
instances 112 47
entities 209 284
triples 1332 1717
Table 3. Characteristics of the two seed ontologies used in benchmarks.
3.2 Results
In order to avoid the discrepancy of last year, all systems were run in the most simple
homogeneous setting. So, this year, we can write anew: All tests have been run entirely
in the same conditions with the same strict protocol.
Evaluations were run on a Debian Linux virtual machine configured with four pro-
cessors and 8GB of RAM running under a Dell PowerEdge T610 with 2*Intel Xeon
Quad Core 2.26GHz E5607 processors and 32GB of RAM, under Linux ProxMox 2
(Debian). All matchers where run under the SEALS client using Java 1.8 and a maxi-
mum heap size of 8GB.
As a result, many systems were not able to properly match the benchmark. Evalua-
tors availability is not unbounded and it was not possible to pay attention to each system
as much as necessary.
Participation From the 21 systems participating to OAEI this year, only 10 systems
were providing results for this track. Several of these systems encountered problems:
However we encountered problems with one very slow matcher (LogMapBio) that
has been run anyway. RiMOM did not terminate, but was able to provide (empty) align-
ments for biblio, not for film. No timeout was explicitly set.
Reported figures are the average of 5 runs. As has already been shown in [20], there
is not much variance in compliance measures across runs.
Compliance Table 4 synthesises the results obtained by matchers.
biblio film
Matcher Prec. F-m. Rec. Prec. F-m. Rec.
edna .35(.58) .41(.54) .51(.50) .43 (.68) .47 (.58) .50 (.50)
AML 1.0 .38 .24 1.0 .32 .20
CroMatcher .96 (.60) .89 (.54) .83 (.50) NaN
Lily .97 (.45) .89 (.40) .83 (.36) .97 (.39) .81 (.31) .70 (.26)
LogMap .93 (.90) .55 (.53) .39 (.37) .83 (.79) .13 (.12) .07 (.06)
LogMapLt .43 .46 .50 .62 .51 .44
PhenoMF .03 .01 .01 .03 .01 .01
PhenoMM .03 .01 .01 .03 .01 .01
PhenoMP .02 .01 .01 .03 .01 .01
XMap .95 (.98) .56 (.57) .40 (.40) .78 (.84) .60 (.62) .49 (.49)
LogMapBio .48 (.48) .32 (.30) .24 (.22) .59 (.58) .07 (.06) .03 (.03)
Table 4. Aggregated benchmark results: Harmonic means of precision, F-measure and recall,
along with their confidence-weighted values.
Systems that participated previously (AML, CroMatcher, Lily, LogMap, LogMapLite,
XMap) still obtain the best results with Lily and CroMatcher still achieving an impressive
.89 F-measure (against .90 and .88 last year). They combine very high precision (.96
and .97) with high recall (.83). The PhenoXX suite of systems return huge but poor
alignments. It is surprising that some of the systems (AML, LogMapLite) do not clearly
outperform edna (our edit distance baseline).
On the film data set (which was not known from the participants when submitting
their systems, and actually have been generated afterwards), the results of biblio are
fully confirmed: (1) those system able to return results were still able to do it besides
CroMatcher and those unable, were still not able; (2) the order between these systems
and their performances are commensurate. Point (1) shows that these are robust sys-
tems. Point (2) shows that the performances of these system are consistent across data
sets, hence we are indeed measuring something. However, (2) has for exception LogMap
and LogMapBio whose precision is roughly preserved but whose recall dramatically
drops. A tentative explanation is that film contains many labels in French and these two
systems rely too much on WordNet. Anyway, these and CroMatcher seem to show some
overfit to biblio.
Polarity Besides LogMapLite, all systems have higher precision than recall as usual
and usually very high precision as shown on the triangle graph for biblio (Figure 2).
This can be compared with last year.
R=1. refalign (b/f)
P=1.
R=.9 P=.9
F=0.9 Lily (b)
R=.8 P=.8
CroMatcher (b)
R=.7 P=.7
F=0.8
Lily (f)
R=.6 P=.6
F=0.7
R=.5 P=.5
F=0.6
R=.4 P=.4
XMap (f)
XMap (b)
F=0.5
LogMap (b)
R=.3 P=.3
AML (b)
R=.2 AML (f) P=.2
LogMapLite (f)
R=.1 P=.1
recall precision
Fig. 2. Triangle view on the benchmark data sets (biblio=(b), film=(f), run 5, non present systems
have too low F-measure, below .5).
The precision/recall graph (Figure 3) confirms that, as usual, there are a level of
recall unreachable by any system and this is where some of them go to catch their good
F-measure.
Concerning confidence-weighted measures, there are two types of systems: those
(CroMatcher, Lily) which obviously threshold their results but keep low confidence val-
ues and those (LogMap, XMap, LogMapBio) which provide relatively faithful measures.
The former shows a strong degradation of the measured values while the latters resist
1.
refalign edna Lily
1.00 0.50 0.82
AML XMap CroMatcher
precision
0.24 0.40 0.81
LogMap LogMapLite LogMapBio
0.38 0.50 0.19
PhenoMF PhenoMM PhenoMP
0.01 0.01 0.01
0.0. recall 1.
Fig. 3. Precision/recall plots on biblio.
very well with XMap even improving its score. This measure which is supposed to re-
ward systems able to provide accurate confidence values is beneficial to these faithful
systems.
Speed Beside LogMapBio which uses alignment repositories on the web to find
matches, all matchers do the task in less than 40 min (for biblio and 12h for film).
There is still a large discrepancy between matchers concerning the time spent from less
than two minutes for LogMapLite, AML and XMap to nearly two hours for LogMapBio
(on biblio).
biblio film
Matcher time stdev F-m./s. time stdev F-m./s.
AML 120 ±13% .32 183 ±1% .17
CroMatcher 1100 ±3% .08 NaN
Lily 2211 ±1% .04 2797 ±1% .03
LogMap 194 ±5% .28 40609 ±33% .00
LogMapLt 96 ±10% .48 116 ±0% .44
PhenoMF 1632 ±8% .00 1798 ±7% .00
PhenoMM 1743 ±7% .00 1909 ±7% .00
PhenoMP 1833 ±7% .00 1835 ±7% .00
XMap 123 ±9% .46 2981 ±21% .02
LogMapBio 54439 ±6% .00 193763419 ±32% .00
Table 5. Aggregated benchmark results: Time (in second), standard deviation on time and points
of F-measure per second spent on the three data sets.
Table 5 provides the average time, time standard deviation and 1/100e F-measure
point provided per second by matchers. The F-measure point provided per second shows
that efficient matchers are, like two years ago, LogMapLite and XMap followed by AML
and LogMap. The correlation between time and F-measure only holds for these systems.
Time taken by systems is, for most of them, far larger on film than biblio and the
deviation from average increased as well.
3.3 Conclusions
This year, there is no increase or decrease of the performance of the best matchers which
are roughly the same as previous years. Precision is still preferred to recall by the best
systems. It seems difficult to other matchers to catch up both in terms of robustness and
performances. This confirms the trend observed last year.
4 Anatomy
The anatomy test case confronts matchers with a specific type of ontologies from the
biomedical domain. We focus on two fragments of biomedical ontologies which de-
scribe the human anatomy5 and the anatomy of the mouse6 . This data set has been used
since 2007 with some improvements over the years.
4.1 Experimental Setting
We conducted experiments by executing each system in its standard setting and we
compare precision, recall, F-measure and recall+. The measure recall+ indicates the
amount of detected non-trivial correspondences. The matched entities in a non-trivial
correspondence do not have the same normalised label. The approach that generates
only trivial correspondences is depicted as baseline StringEquiv in the following section.
We ran the systems on a server with 3.46 GHz (6 cores) and 8GB RAM allocated
to each matching system. Further, we used the SEALS client to execute our evaluation.
However, we slightly changed the way precision and recall are computed, i.e., the results
generated by the SEALS client vary in some cases by 0.5% compared to the results
presented below. In particular, we removed trivial correspondences in the oboInOwl
namespace like:
http://...oboInOwl#Synonym = http://...oboInOwl#Synonym
as well as correspondences expressing relations different from equivalence. Using the
Pellet reasoner we also checked whether the generated alignment is coherent, i.e., that
there are no unsatisfiable classes when the ontologies are merged with the alignment.
4.2 Results
Table 6 reports all the 13 participating systems that could generate an alignment. As
previous years some of the systems participated with different versions. LogMap partic-
ipated with LogMap, LogMapBio and a lightweight version LogMapLite that uses only
some core components. Similarly, DKP-AOM also participated with two versions, DKP-
AOM and DKP-AOM-Lite. Several systems participate in the anatomy track for the first
time. These are Alin, FCA Map, DLPHOM and LYAM. There are also systems having
been participant for several years in a row. LogMap is a constant participant since 2011.
5
http://www.cancer.gov/cancertopics/cancerlibrary/
terminologyresources/
6
http://www.informatics.jax.org/searches/AMA_form.shtml
AML and XMap joined the track in 2013. DKP-AOM, Lily and CroMatcher participate
for the second year in a row in this track. Lily participated in the track back in 2011.
CroMatcher participated in 2013 but did not produce an alignment within the given
time frame. Thus, this year we have 10 different systems (not counting different ver-
sions) which generated an alignment. For more details, we refer the reader to the papers
presenting the systems.
Matcher Runtime Size Precision F-measure Recall Recall+ Coherent
√
AML 47 1493 0.95 0.943 0.936 0.832
CroMatcher 573 1442 0.949 0.925 0.902 0.773 -
√
XMap 45 1413 0.929 0.896 0.865 0.647
√
LogMapBio 758 1531 0.888 0.892 0.896 0.728
FCA Map 117 1361 0.932 0.882 0.837 0.578 -
√
LogMap 24 1397 0.918 0.88 0.846 0.593
LYAM 799 1539 0.863 0.869 0.876 0.682 -
Lily 272 1382 0.87 0.83 0.794 0.515 -
LogMapLite 20 1147 0.962 0.828 0.728 0.288 -
StringEquiv - 946 0.997 0.766 0.622 0.000 -
LPHOM 1601 1555 0.709 0.718 0.727 0.497 -
√
Alin 306 510 0.996 0.501 0.335 0.0
√
DKP-AOM-Lite 372 207 0.99 0.238 0.135 0.0
√
DKP-AOM 379 207 0.99 0.238 0.135 0.0
Table 6. Comparison, ordered by F-measure, against the reference alignment, runtime is mea-
sured in seconds, the “size” column refers to the number of correspondences in the generated
alignment.
Unlike the last two editions of the track when 6 systems generated an alignment in
less than 100 seconds, this year only 4 of them were able to complete the alignment
task in this time frame. These are AML, XMap, LogMap and LogMapLite. Similarly to
the last 4 years LogMapLite has the shortest runtime, followed by LogMap, XMap and
AML. Depending on the specific version of the systems, they require between 20 and 50
seconds to match the ontologies. The table shows that there is no correlation between
quality of the generated alignment in terms of precision and recall and required runtime.
This result has also been observed in previous OAEI campaigns.
The table also shows the results for precision, recall and F-measure. In terms of
F-measure, the top 5 ranked systems are AML, CroMatcher, XMap, LogMapBio and
FCA Map. LogMap is sixth with a F-measure very close to FCA Map. All the long-term
participants in the track showed comparable results (in term or F-measure) to their last
year’s results and at least as good as the results of the best systems in OAEI 2007-2010.
LogMap and XMap generated the same number of correspondences in their alignment
(XMap generated one correspondence more). AML and LogMapBio generated a slightly
different number—16 correspondences more for AML and 18 less for LogMapBio.
The results for the DKP-AOM systems are identical this year; by contrast, last year
the lite version performed significantly better in terms of the observed measures. While
Lily had improved its 2015 results in comparison to 2011 (precision: from 0.814 to
0.870, recall: from 0.734 to 0.793, and F-measure: from 0.772 to 0.830), this year
it performed similarly to last year. CroMatcher improved its results in comparison to
last year. Out of all systems participating in the anatomy track CroMatcher showed the
largest improvement in the observed measures in comparison to its values from the
previous edition of the track.
Comparing the F-measures of the new systems, FCA Map (0.882) scored very close
to one of the tracks’ long-term participants LogMap. Another of the new systems—
LYAM—also achieved a good F-measure (0.869) which ranked sixth. As for the other
two systems, LPHOM achieved a slightly lower F-measure than the baseline (StringE-
quiv) whereas Alin was considerably below the baseline.
This year, 9 out of 13 systems achieved an F-measure higher than the baseline which
is based on (normalised) string equivalence (StringEquiv in the table). This is a slightly
better result (percentage-wise) than last year’s (9 out of 15) and similar to 2014’s (7 out
of 10). Two of the new participants in the track and the two DKP-AOM systems achieved
an F-measure lower than the baseline. LPHOM scored under the StringEquiv baseline but
at the same time it is the system that produced the highest number of correspondences.
Its precision is significantly lower than the other three systems which scored under the
baseline and generated only trivial correspondences.
This year seven systems produced coherent alignments which is comparable to the
last two years, when 7 out of 15 and 5 out of 10 systems achieved this. From the five
best systems only FCA Map produced an incoherent alignment.
4.3 Conclusions
Like for OAEI in general, the number of participating systems in the anatomy track
this year was lower than in 2015 and 2013 but higher than in 2014, and there was a
combination of newly-joined systems and long-term participants.
The systems that participated in the previous edition scored similarly to their pre-
vious results, indicating that no substantial developments were made with regard to
this track. Of the newly-joined systems, (FCA Map and LYAM) ranked 4th and 6th with
respect to the F-measure.
5 Conference
The conference test case requires matching several moderately expressive ontologies
from the conference organisation domain.
5.1 Test data
The data set consists of 16 ontologies in the domain of organising conferences. These
ontologies have been developed within the OntoFarm project7 .
The main features of this test case are:
– Generally understandable domain. Most ontology engineers are familiar with or-
ganising conferences. Therefore, they can create their own ontologies as well as
evaluate the alignments among their concepts with enough erudition.
– Independence of ontologies. Ontologies were developed independently and based
on different resources, they thus capture the issues in organising conferences from
different points of view and with different terminologies.
7
http://owl.vse.cz:8080/ontofarm/
– Relative richness in axioms. Most ontologies were equipped with OWL DL axioms
of various kinds; this opens a way to use semantic matchers.
Ontologies differ in their numbers of classes and properties, in expressivity, but also
in underlying resources.
5.2 Results
We provide results in terms of F-measure, comparison with baseline matchers and re-
sults from previous OAEI editions and precision/recall triangular graph based on sharp
reference alignments. This year we can provide comparison between OAEI editions
of results based on the uncertain version of reference alignment and on violations of
consistency and conservativity principles.
Evaluation based on sharp reference alignments We evaluated the results of partic-
ipants against blind reference alignments (labelled as rar2). This includes all pairwise
combinations between 7 different ontologies, i.e., 21 alignments.
These reference alignments have been made in two steps. First, we have generated
them as a transitive closure computed on the original reference alignments. In order to
obtain a coherent result, conflicting correspondences, i.e., those causing unsatisfiabil-
ity, have been manually inspected and removed by evaluators. The resulting reference
alignments are labelled as ra2. Second, we detected violations of conservativity us-
ing the approach from [39] and resolved them by an evaluator. The resulting reference
alignments are labelled as rar2. As a result, the degree of correctness and completeness
of the new reference alignments is probably slightly better than for the old one. How-
ever, the differences are relatively limited. Whereas the new reference alignments are
not open, the old reference alignments (labeled as ra1 on the conference web page) are
available. These represent close approximations of the new ones.
Table 7 shows the results of all participants with regard to the reference alignment
rar2. F0.5 -measure, F1 -measure and F2 -measure are computed for the threshold that
provides the highest average F1 -measure. F1 is the harmonic mean of precision and
recall where both are equally weighted; F2 weights recall higher than precision and
F0.5 weights precision higher than recall. The matchers shown in the table are ordered
according to their highest average F1 -measure. We employed two baseline matchers.
edna (string edit distance matcher) is used within the benchmark test case and with
regard to performance it is very similar as the previously used baseline2 in the con-
ference track; StringEquiv is used within the anatomy test case. This year these base-
lines divide matchers into two performance groups. The first group consists of match-
ers (CroMatcher, AML, LogMap, XMap, LogMapBio, FCA Map, DKP-AOM, NAISC and
LogMapLite) having better (or the same) results than both baselines in terms of highest
average F1 -measure. Other matchers (Lily, LPHOM, Alin and LYAM) performed worse
than both baselines. The performance of all matchers (except LYAM) regarding their
precision, recall and F1 -measure is visualised in Figure 4. Matchers are represented as
squares or triangles. Baselines are represented as circles.
Further, we evaluated the performance of matchers separately on classes and prop-
erties. We compared the position of tools within overall performance groups and within
only classes and only properties performance groups. We observed that while the posi-
tion of matchers changed slightly in overall performance groups in comparison with
Matcher Prec. F0.5 -m. F1 -m. F2 -m. Rec. Inc.Align. Conser.V. Consist.V.
CroMatcher 0.74 0.72 0.69 0.67 0.65 8 98 25
AML 0.78 0.74 0.69 0.65 0.62 0 52 0
LogMap 0.77 0.72 0.66 0.6 0.57 0 30 0
XMap 0.8 0.73 0.65 0.59 0.55 0 23 0
LogMapBio 0.72 0.67 0.61 0.56 0.53 0 30 0
FCA Map 0.71 0.65 0.59 0.53 0.5 12 46 150
DKP-AOM 0.76 0.68 0.58 0.51 0.47 0 35 0
NAISC 0.77 0.67 0.57 0.49 0.45 20 321 701
edna 0.74 0.66 0.56 0.49 0.45
LogMapLite 0.68 0.62 0.56 0.5 0.47 6 99 81
StringEquiv 0.76 0.65 0.53 0.45 0.41
Lily 0.54 0.53 0.52 0.51 0.5 13 148 167
LPHOM 0.69 0.57 0.46 0.38 0.34 0 0 0
Alin 0.87 0.59 0.4 0.3 0.26 0 0 0
LYAM 0.4 0.31 0.23 0.18 0.16 1 75 3
Table 7. The highest average F[0.5|1|2] -measure and their corresponding precision and recall for
each matcher with its F1 -optimal threshold (ordered by F1 -measure). Inc.Align. means number
of incoherent alignments. Conser.V. means total number of all conservativity principle violations.
Consist.V. means total number of all consistency principle violations.
only classes performance groups, a couple of matchers (DKP-AOM and FCA Map)
worsen their position from overall performance groups with regard to their position
in only properties performance groups due to the fact that they do not match proper-
ties at all (Alin and Lily also fall into this category). More details about these evaluation
modalities are on the conference web page.
Comparison with previous years with regard to rar2 Seven matchers also participated
in this test case in OAEI 2015. The largest improvement was achieved by CroMatcher
(precision increased from .57 to .74 and recall increased from .47 to .65).
Evaluation based on uncertain version of reference alignments The confidence val-
ues of all matches in the sharp reference alignments for the conference track are all 1.0.
For the uncertain version of this track, the confidence value of a match has been set
equal to the percentage of a group of people who agreed with the match in question
(this uncertain version is based on the reference alignment labeled ra1). One key thing
to note is that the group was only asked to validate matches that were already present in
the existing reference alignments – so some matches had their confidence value reduced
from 1.0 to a number near 0, but no new match was added.
There are two ways that we can evaluate matchers according to these “uncertain”
reference alignments, which we refer to as discrete and continuous. The discrete evalu-
ation considers any match in the reference alignment with a confidence value of 0.5 or
greater to be fully correct and those with a confidence less than 0.5 to be fully incorrect.
Similarly, a matcher’s match is considered a “yes” if the confidence value is greater than
or equal to the matcher’s threshold and a “no” otherwise. In essence, this is the same as
the “sharp” evaluation approach, except that some matches have been removed because
less than half of the crowdsourcing group agreed with them. The continuous evaluation
Alin
AML
CroMatcher
DKP-AOM
FCA-Map
F1 -measure=0.7 Lily
LogMap
F1 -measure=0.6 LogMapBio
LogMapLite
F1 -measure=0.5
LPHOM
NAISC
XMap
edna
StringEquiv
rec=1.0 rec=.8 rec=.6 pre=.6 pre=.8 pre=1.0
Fig. 4. Precision/recall triangular graph for the conference test case. Dotted lines depict level
of precision/recall while values of F1 -measure are depicted by areas bordered by corresponding
lines F1 -measure=0.[5|6|7].
strategy penalises a matcher more if it misses a match on which most people agree than
if it misses a more controversial match. For instance, if A ≡ B with a confidence of
0.85 in the reference alignment and a matcher gives that correspondence a confidence of
0.40, then that is counted as 0.85 × 0.40 = 0.34 of a true positive and 0.85–0.40 = 0.45
of a false negative.
Out of the 13 matchers, three (DKP-AOM, FCA-Map and LogMapLite) use 1.0 as the
confidence values for all matches they identify. Two of the remaining ten (Alin and Cro-
Matcher) have some variation in confidence values, though the majority are 1.0. The rest
of the systems have a fairly wide variation of confidence values. Last year, the majority
of these values were near the upper end of the [0,1] range. This year we see much more
variation in the average confidence values. For example, LopMap’s confidence values
range from 0.29 to 1.0 and average 0.78 whereas Lily’s range from 0.22 to 0.41 with an
average of 0.33.
Discussion When comparing the performance of the matchers on the uncertain refer-
ence alignments versus that on the sharp version, we see that in the discrete case all
matchers performed slightly better. Improvement in F-measure ranged from 1 to 8 per-
centage points over the sharp reference alignment. This was driven by increased recall,
which is a result of the presence of fewer “controversial” matches in the uncertain ver-
sion of the reference alignment.
The performance of most matchers is similar regardless of whether a discrete or
continuous evaluation methodology is used (provided that the threshold is optimised to
achieve the highest possible F-measure in the discrete case). The primary exceptions
Sharp Discrete Continuous
Matcher Prec. F1 -m. Rec. Prec. F1 -m. Rec. Prec. F1 -m. Rec.
Alin 0.89 0.40 0.26 0.89 0.48 0.33 0.89 0.48 0.33
AML 0.84 0.74 0.66 0.79 0.78 0.77 0.80 0.77 0.74
CroMatcher 0.79 0.73 0.68 0.71 0.74 0.77 0.72 0.74 0.77
DKP-AOM 0.82 0.62 0.50 0.78 0.67 0.59 0.78 0.69 0.61
FCA-Map 0.75 0.61 0.52 0.71 0.66 0.61 0.69 0.65 0.61
Lily 0.59 0.56 0.53 0.59 0.57 0.56 0.59 0.32 0.22
LogMap 0.82 0.69 0.59 0.78 0.73 0.68 0.80 0.67 0.57
LogMapBio 0.77 0.65 0.56 0.73 0.68 0.64 0.75 0.62 0.53
LogMapLite 0.73 0.59 0.50 0.73 0.67 0.62 0.72 0.67 0.63
LPHOM 0.76 0.47 0.34 0.81 0.59 0.46 0.48 0.47 0.47
Light YAM++ 0.38 0.22 0.15 0.35 0.24 0.18 0.09 0.15 0.38
NAISC 0.85 0.61 0.47 0.87 0.69 0.57 0.34 0.45 0.68
XMap 0.85 0.68 0.57 0.81 0.73 0.67 0.83 0.74 0.67
Table 8. F-measure, precision, and recall of the different matchers when evaluated using the sharp
(ra1), discrete uncertain and continuous uncertain metrics.
to this are Lily and NAISC. These matchers perform significantly worse when evaluated
using the continuous version of the metrics. In Lily’s case, this is because it assigns very
low confidence values to some matches in which the labels are equivalent strings, which
many crowdsourcers agreed with unless there was a compelling technical reason not to.
This hurts recall, but using a low threshold value in the discrete version of the evaluation
metrics “hides” this problem. NAISC has the opposite issue: it assigns relatively high
confidence values to some matches that most people disagree with, such as “Assistant”
and “Listener” (confidence value of 0.89). This hurts precision in the continuous case,
but is taken care of by using a high threshold value (1.0) in the discrete case.
Seven matchers from this year also participated last year, and thus we are able to
make some comparisons over time. The F-measures of all matchers either held constant
or improved when evaluated against the uncertain reference alignments. Most matchers
made modest gains (in the neighborhood of 1 to 6 percentage points). CroMatcher made
the largest improvement, and it is now the second-best matcher when evaluated in this
way. AgreementMakerLight remains the top performer.
Perhaps more importantly, the difference in the performance of most matchers be-
tween the discrete and continuous evaluation has shrunk between this year and last year.
This is an indication that more matchers are providing confidence values that reflect the
disagreement of humans on various matches.
Evaluation based on violations of consistency and conservativity principles We
performed evaluation based on detection of conservativity and consistency violations
[39,40]. The consistency principle states that correspondences should not lead to un-
satisfiable classes in the merged ontology; the conservativity principle states that cor-
respondences should not introduce new semantic relationships between concepts from
one of the input ontologies.
Table 7 summarises statistics per matcher. The table shows the number of unsat-
isfiable TBoxes after the ontologies are merged (Inc. Align.), the total number of all
conservativity principle violations within all alignments (Conser.V.) and the total num-
ber of all consistency principle violations (Consist.V.).
Seven tools (Alin, AML, DKP-AOM, LogMap, LogMapBio, LPHOM and XMap) have
no consistency principle violations (in comparison to five last year) and one tool (LYAM)
generated only one incoherent alignment. There are two tools (Alin, LPHOM) that have
no conservativity principle violations, and four more that have an average of only
one conservativity principle violation (XMap, LogMap, LogMapBio and DKP-AOM). We
should note that these conservativity principle violations can be “false positives” since
the entailment in the aligned ontology can be correct although it was not derivable in
the single input ontologies.
In conclusion, this year eight matchers performed better than both baselines on ref-
erence alignments which is not only consistent but also conservative. Further, this year
seven matchers generated coherent alignments (against five matchers last year and four
matchers the year before). This confirms the trend that increasingly matchers gener-
ate coherent alignments. Based on the uncertain reference alignments, more matchers
are providing confidence values that reflect the disagreement of humans on various
matches.
6 Large biomedical ontologies (largebio)
The largebio test case requires to match the large and semantically rich biomedical
ontologies FMA, SNOMED-CT, and NCI, which contain 78,989, 306,591 and 66,724
classes, respectively.
6.1 Test data
The test case has been split into three matching problems: FMA-NCI, FMA-SNOMED
and SNOMED-NCI. Each matching problem has been further divided in 2 tasks involv-
ing differently sized fragments of the input ontologies: small overlapping fragments
versus whole ontologies (FMA and NCI) or large fragments (SNOMED-CT).
The UMLS Metathesaurus [5] has been selected as the basis for reference align-
ments. UMLS is currently the most comprehensive effort for integrating independently-
developed medical thesauri and ontologies, including FMA, SNOMED-CT, and NCI.
Although the standard UMLS distribution does not directly provide alignments (in
the sense of [21]) between the integrated ontologies, it is relatively straightforward to
extract them from the information provided in the distribution files (see [25] for details).
It has been noticed, however, that although the creation of UMLS alignments com-
bines expert assessment and auditing protocols they lead to a significant number of
logical inconsistencies when integrated with the corresponding source ontologies [25].
Since alignment coherence is an aspect of ontology matching that we aim to pro-
mote, in previous editions we provided coherent reference alignments by refining the
UMLS mappings using the Alcomo (alignment) debugging system [31], LogMap’s
(alignment) repair facility [24], or both [26].
However, concerns were raised about the validity and fairness of applying auto-
mated alignment repair techniques to make reference alignments coherent [35]. It is
clear that using the original (incoherent) UMLS alignments would be penalising to on-
tology matching systems that perform alignment repair. However, using automatically
repaired alignments would penalise systems that do not perform alignment repair and
also systems that employ a repair strategy that differs from that used on the reference
alignments [35].
Thus, as of the 2014 edition, we arrived at a compromising solution that should be
fair to all ontology matching systems. Instead of repairing the reference alignments as
normal, by removing correspondences, we flagged the incoherence-causing correspon-
dences in the alignments by setting the relation to “?” (unknown). These “?” corre-
spondences will neither be considered as positive nor as negative when evaluating the
participating ontology matching systems, but will simply be ignored. This way, systems
that do not perform alignment repair are not penalised for finding correspondences that
(despite causing incoherences) may or may not be correct, and systems that do perform
alignment repair are not penalised for removing such correspondences.
To ensure that this solution was as fair as possible to all alignment repair strategies,
we flagged as unknown all correspondences suppressed by any of Alcomo, LogMap or
AML [?], as well as all correspondences suppressed from the reference alignments of
last year’s edition (using Alcomo and LogMap combined). Note that, we have used the
(incomplete) repair modules of the above mentioned systems.
The flagged UMLS-based reference alignment for the OAEI 2016 campaign is sum-
marised in Table 9.
Reference alignment “=” corresp. “?” corresp.
FMA-NCI 2,686 338
FMA-SNOMED 6,026 2,982
SNOMED-NCI 17,210 1,634
Table 9. Respective sizes of reference alignments
6.2 Evaluation setting, participation and success
We have run the evaluation on a Ubuntu Laptop with an Intel Core i7-4600U CPU @
2.10GHz x 4 and allocating 15Gb of RAM. Precision, recall and F-measure have been
computed with respect to the UMLS-based reference alignment. Systems have been
ordered in terms of F-measure.
This year, out of the 21 systems participating in OAEI 2016, 13 were registered to
participate in the largebio track, and 11 of these were able to cope with at least one
of the largebio tasks within a 2 hour time frame. However, only 6 systems were able
to complete more than one task, and only 4 systems completed all 6 tasks in this time
frame.
6.3 Background knowledge
Regarding the use of background knowledge, LogMap-Bio uses BioPortal as a mediating
ontology provider, that is, it retrieves from BioPortal the most suitable top-10 ontologies
for the matching task.
LogMap uses normalisations and spelling variants from the general (biomedical)
purpose UMLS Lexicon.
AML has three sources of background knowledge which can be used as mediators
between the input ontologies: the Uber Anatomy Ontology (Uberon), the Human Dis-
ease Ontology (DOID) and the Medical Subject Headings (MeSH).
FMA-NCI FMA-SNOMED SNOMED-NCI
System Average #
Task 1 Task 2 Task 3 Task 4 Task 5 Task 6
LogMapLite 1 10 2 18 8 18 10 6
AML 35 72 98 166 537 376 214 6
LogMap 10 80 60 433 177 699 243 6
LogMapBio 1,712 1,188 1,180 2,156 3,757 4,322 2,386 6
XMap 17 116 54 366 267 - 164 5
FCA-Map 236 - 1,865 - - - 1,051 2
Lily 699 - - - - - 699 1
LYAM 1,043 - - - - - 1,043 1
DKP-AOM 1,547 - - - - - 1,547 1
DKP-AOM-Lite 1,698 - - - - - 1,698 1
Alin 5,811 - - - - - 5,811 1
# Systems 11 6 5 5 5 4 1,351 36
Table 10. System runtimes (s) and task completion.
XMap uses synonyms provided by the UMLS Metathesaurus. Note that matching
systems using UMLS Metathesaurus as background knowledge will have a notable
advantage since the largebio reference alignment is also based on the UMLS Metathe-
saurus.
6.4 Alignment coherence
Together with precision, recall, F-measure and run times we have also evaluated the
coherence of alignments. We report (1) the number of unsatisfiabilities when reasoning
with the input ontologies together with the computed alignments, and (2) the ratio of
unsatisfiable classes with respect to the size of the union of the input ontologies.
We have used the OWL 2 reasoner HermiT [33] to compute the number of unsatis-
fiable classes. For the cases in which HermiT could not cope with the input ontologies
and the alignments (in less than 2 hours) we have provided a lower bound on the number
of unsatisfiable classes (indicated by ≥) using the OWL 2 EL reasoner ELK [27].
In this OAEI edition, only three distinct systems have shown alignment repair fa-
cilities: AML, LogMap and its LogMap-Bio variant, and XMap (which reuses the repair
techniques from Alcomo [31]). Tables 11-12 (see last two columns) show that even the
most precise alignment sets may lead to a huge number of unsatisfiable classes. This
proves the importance of using techniques to assess the coherence of the generated
alignments if they are to be used in tasks involving reasoning. We encourage ontology
matching system developers to develop their own repair techniques or to use state-of-
the-art techniques such as Alcomo [31], the repair module of LogMap (LogMap-Repair)
[24] or the repair module of AML [?], which have worked well in practice [26,22].
6.5 Runtimes and task completion
Table 10 shows which systems were able to complete each of the matching tasks in
less than 24 hours and the required computation times. Systems have been ordered with
respect to the number of completed tasks and the average time required to complete
them. Times are reported in seconds.
The last column reports the number of tasks that a system could complete. For
example, 8 system were able to complete all six tasks. The last row shows the number
Task 1: small FMA and NCI fragments
Scores Incoherence
System Time (s) # Corresp.
Prec. F-m. Rec. Unsat. Degree
XMap8 17 2,649 0.98 0.94 0.90 2 0.019%
FCA-Map 236 2,834 0.95 0.94 0.92 4,729 46.0%
AML 35 2,691 0.96 0.93 0.90 2 0.019%
LogMap 10 2,747 0.95 0.92 0.90 2 0.019%
LogMapBio 1,712 2,817 0.94 0.92 0.91 2 0.019%
LogMapLite 1 2,483 0.97 0.89 0.82 2,045 19.9%
Average 1,164 2,677 0.85 0.80 0.78 2,434 23.7%
LYAM 1,043 3,534 0.72 0.80 0.89 6,880 66.9%
Lily 699 3,374 0.60 0.66 0.72 9,273 90.2%
Alin 5,811 1,300 1.00 0.62 0.46 0 0.0%
DKP-AOM-Lite 1,698 2,513 0.65 0.61 0.58 1,924 18.7%
DKP-AOM 1,547 2,513 0.65 0.61 0.58 1,924 18.7%
Task 2: whole FMA and NCI ontologies
Scores Incoherence
System Time (s) # Corresp.
Prec. F-m. Rec. Unsat. Degree
XMap8 116 2,681 0.90 0.87 0.85 9 0.006%
AML 72 2,968 0.84 0.85 0.87 10 0.007%
LogMap 80 2,693 0.85 0.83 0.80 9 0.006%
LogMapBio 1,188 2,924 0.82 0.83 0.84 9 0.006%
Average 293 2,948 0.82 0.82 0.84 5,303 3.6%
LogMapLite 10 3,477 0.67 0.74 0.82 26,478 18.1%
Table 11. Results for the FMA-NCI matching problem.
of systems that could finish each of the tasks. The tasks involving SNOMED were
also harder with respect to both computation times and the number of systems that
completed the tasks.
6.6 Results for the FMA-NCI matching problem
Table 11 summarises the results for the tasks in the FMA-NCI matching problem.
XMap and FCA-Map achieved the highest F-measure in Task 1; XMap and AML
in Task 2. Note however that the use of background knowledge based on the UMLS
Metathesaurus has an important impact in the performance of XMap8 . The use of back-
ground knowledge led to an improvement in recall from LogMap-Bio over LogMap in
both tasks, but this came at the cost of precision, resulting in the two variants of the
system having identical F-measures.
Note that the effectiveness of the systems decreased from Task 1 to Task 2. One rea-
son for this is that with larger ontologies there are more plausible mapping candidates,
and thus it is harder to attain both a high precision and a high recall. Another reason is
that the very scale of the problem constrains the matching strategies that systems can
8
Uses background knowledge based on the UMLS Metathesaurus which is the base of the
largebio reference alignments.
Task 3: small FMA and SNOMED fragments
Scores Incoherence
System Time (s) # Corresp.
Prec. F-m. Rec. Unsat. Degree
XMap8 54 7,311 0.99 0.91 0.85 0 0.0%
FCA-Map 1,865 7,649 0.94 0.86 0.80 14,603 61.8%
AML 98 6,554 0.95 0.82 0.73 0 0.0%
LogMapBio 1,180 6,357 0.94 0.80 0.70 1 0.004%
LogMap 60 6,282 0.95 0.80 0.69 1 0.004%
Average 543 5,966 0.96 0.76 0.66 2,562 10.8%
LogMapLite 2 1,644 0.97 0.34 0.21 771 3.3%
Task 4: whole FMA ontology with SNOMED large fragment
Scores Incoherence
System Time (s) # Corresp.
Prec. F-m. Rec. Unsat. Degree
XMap8 366 7,361 0.97 0.90 0.84 0 0.0%
AML 166 6,571 0.88 0.77 0.69 0 0.0%
LogMap 433 6,281 0.84 0.72 0.63 0 0.0%
LogMapBio 2,156 6,520 0.81 0.71 0.64 0 0.0%
Average 627 5,711 0.87 0.69 0.60 877 0.4%
LogMapLite 18 1,822 0.85 0.34 0.21 4,389 2.2%
Table 12. Results for the FMA-SNOMED matching problem.
employ: AML for example, foregoes its matching algorithms that are computationally
more complex when handling very large ontologies, due to efficiency concerns.
The size of Task 2 proves a problem for several systems, which were unable to
complete it within the allotted time: FCA-Map, LYAM, LiLy, Alin, DKP-AOM-Lite and
DKP-AOM.
6.7 Results for the FMA-SNOMED matching problem
Table 12 summarises the results for the tasks in the FMA-SNOMED matching problem.
XMap produced the best results in terms of both recall and F-measure in Task 3
and Task 4, but again, we must highlight that it uses background knowledge based on
the UMLS Metathesaurus. Among the other systems, FCA-Map and AML achieved the
highest F-measure in Tasks 3 and 4, respectively.
Overall, the quality of the results was lower than that observed in the FMA-NCI
matching problem, as the matching problem is considerably larger. Indeed, several sys-
tems were unable to complete even the smaller Task 3 within the allotted time: LYAM,
LiLy, Alin, DKP-AOM-Lite and DKP-AOM.
Like in the FMA-NCI matching problem, the effectiveness of all systems decreases
as the ontology size increases from Task 3 to Task 4; FCA-Map could complete the
former but not the latter.
6.8 Results for the SNOMED-NCI matching problem
Table 13 summarises the results for the tasks in the SNOMED-NCI matching problem.
AML achieved the best results in terms of both recall and F-measure in Tasks 5 and
6, while LogMap and AML achieved the best results in terms of precision in Tasks 5 and
6, respectively.
Task 5: small SNOMED and NCI fragments
Scores Incoherence
System Time (s) # Corresp.
Prec. F-m. Rec. Unsat. Degree
AML 537 13,584 0.90 0.80 0.71 0 0.0%
LogMap 177 12,371 0.92 0.77 0.66 0 0.0%
LogMapBio 3,757 12,960 0.90 0.77 0.68 0 0.0%
Average 949 13,302 0.91 0.75 0.64 ≥12,090 ≥16.1%
XMap8 267 16,657 0.91 0.70 0.56 0 0.0%
LogMapLite 8 10,942 0.89 0.69 0.57 ≥60,450 ≥80.4%
Task 6: whole NCI ontology with SNOMED large fragment
Scores Incoherence
System Time (s) # Corresp.
Prec. F-m. Rec. Unsat. Degree
AML 376 13,175 0.90 0.77 0.67 ≥2 ≥0.001%
LogMapBio 4,322 13,477 0.84 0.72 0.64 ≥6 ≥0.003%
Average 1,353 12,942 0.85 0.72 0.62 37,667 19.9%
LogMap 699 12,222 0.87 0.71 0.60 ≥4 ≥0.002%
LogMapLite 18 12,894 0.80 0.66 0.57 ≥150,656 ≥79.5%
Table 13. Results for the SNOMED-NCI matching problem.
The overall performance of the systems was lower than in the FMA-SNOMED case,
as this test case is even larger. As such, LiLy, DKP-AOM-Lite, DKP-AOM, FCA-Map, Alin
and LYAM could not complete even the smaller Task 5 within 2 hours.
As in the previous matching problems, effectiveness decreases as the ontology size
increases, and XMap completed Task 5 but failed to complete Task 6 within the given
time frame.
Unlike in the FMA-NCI and FMA-SNOMED matching problems, the use of the
UMLS Metathesaurus did not positively impact the performance of XMap, which ob-
tained lower results than expected.
7 Disease and Phenotype Track (phenotype)
The Pistoia Alliance Ontologies Mapping project team9 has organised this track based
on a real use case where it is required to find alignments between disease and pheno-
type ontologies. Specifically, the selected ontologies are the Human Phenotype Ontol-
ogy (HPO), the Mammalian Phenotype Ontology (MP), the Human Disease Ontology
(DOID), and the Orphanet and Rare Diseases Ontology (ORDO).
7.1 Test data
There are two tasks in this track which comprise the pairwise alignment of:
– Human Phenotype Ontology (HPO) to Mammalian Phenotype Ontology (MP), and
– Human Disease Ontology (DOID) to Orphanet and Rare Diseases Ontology
(ORDO).
The first task is important for translational science, since mammal model animals
such as mice are widely used to study human diseases and their underlying genetics.
9
http://www.pistoiaalliance.org/projects/ontologies-mapping/
Mapping human phenotypes to mammalian phenotypes greatly facilitates the extrapo-
lation from model animals to humans.
The second task is critical to ensure interoperability between two disease ontolo-
gies: the more generic DOID and the more specific ORDO, in the domain of rare hu-
man diseases. These are fundamental for understanding how genetic variation can cause
disease.
Currently, mappings between these ontologies are mostly curated by bioinformatics
and disease experts who would benefit from the use of automated ontology matching
algorithms into their workflows.
7.2 Evaluation setting
We have run the evaluation on a Ubuntu Laptop with an Intel Core i7-4600U CPU @
2.10GHz x 4, allocating 15Gb of RAM.
In the OAEI 2016 phenotype track, 11 out of the 21 participating OAEI 2016 sys-
tems have been able to cope with at least one of the tasks within a 24 hour time frame.
7.3 Evaluation criteria
Systems have been evaluated according to the following criteria:
– Semantic precision and recall with respect to silver standards automatically gen-
erated by voting based on the outputs of all participating systems (we have used
vote=2 and vote=3)10 .
– Semantic recall with respect to manually generated correspondences for three areas
(carbohydrate, obesity and breast cancer).
– Manual assessment of a subset of the generated correspondences, specially the ones
that are not suggested by other systems, i.e., unique mapping.
We have used the OWL 2 reasoner HermiT to calculate the semantic precision and
recall. For example, a positive hit will mean that a mapping in the reference has been
(explicitly) included in the output mappings or it can be inferred using reasoning from
the input ontologies and the output mappings. The use of semantic values for preci-
sion and recall also allowed us to provide a fair comparison for the systems PhenoMF,
PhenoMM and PhenoMP which discover many subsumption mappings that are not ex-
plicitly in the silver standards but may still be valid, i.e., inferred.
7.4 Use of background knowledge
LogMapBio uses BioPortal as a mediating ontology provider, that is, it retrieves from
BioPortal the most suitable top-10 ontologies for the matching task.
LogMap uses normalisations and spelling variants from the general (biomedical)
purpose UMLS Lexicon.
AML has three sources of background knowledge which can be used as mediators
between the input ontologies: the Uber Anatomy Ontology (Uberon), the Human Dis-
ease Ontology (DOID) and the Medical Subject Headings (MeSH). Additionally, for
the HPO-MP test case, it uses the logical definitions of both ontologies, which define
10
When there are several systems of the same family, only one of them votes for avoiding bias.
There still can be some bias through systems exploiting the same resource, e.g., UMLS.
Table 14. Results against silver standard with vote 2 and 3.
some of their classes as being a combination of an anatomic term, i.e., a class from ei-
ther FMA or Uberon, with a phenotype modifier term, i.e., a class from the Phenotypic
Quality Ontology.
XMap uses synonyms provided by the UMLS Metathesaurus.
PhenoMM, PhenoMF and PhenoMP rely on different versions of the Phe-
nomeNET11 ontology with variable complexity.
7.5 Results
AML, FCA-Map, LogMap, LogMapBio, and PhenoMF produced the most complete re-
sults according to both the automatic and manual evaluation.
Results against the silver standards The silver standards with vote 2 and 3 for HP-MP
contain 2,308 and 1,588 mappings, respectively; while for DOID-ORDO they include
1,883 and 1,617 mappings respectively. Table 14 shows the results achieved by each
of the participating systems. We deliberately did not rank the systems since the silver
standards only allow us to assess how systems perform in comparison with one another.
On the one hand, some of the mappings in the silver standard may be erroneous (false
positives), as all it takes for that is that 2 or 3 systems agree on part of the erroneous
mappings they find. On the other hand, the silver standard is not complete, as there
will likely be correct mappings that no system is able to find, and as we will show in
the manual evaluation, there are a number of mappings found by only one system (and
therefore not in the silver standard) which are correct. Nevertheless, the results with
respect to the silver standards do provide some insights into the performance of the
systems, which is why we highlighted in the table the 5 systems that produce results
closest to the silver standards: AML, FCA-Map, LogMap, LogMapBio, and PhenoMF.
Results against manually created mappings The manually generated mappings for
three areas (carbohydrate, obesity and breast cancer) include 29 mappings between HP
and MP and 60 mappings between DOID and ORDO. Most of them representing sub-
sumption relationships. Table 15 shows the results in terms of recall for each of the sys-
tems. PhenoMF, PhenoMP and PhenoMM achieve very good results for HP-MP since
11
http://aber-owl.net/ontology/PhenomeNET
Table 15. Recall against manually created mappings.
Table 16. Unique mappings in the HP-MP task.
Table 17. Unique mappings in the DOID-ORDO task.
they discover a large number of subsumption mappings. However, for DOID-ORDO
only LogMap, LogMapBio and DisMatch discover some of the mappings in the curated
set.
Manual assessment of unique mappings Tables 16 and 17 show the precision results
of the manual assessment of the unique mappings generated by the participating sys-
tems. Unique mappings are correspondences that no other system (explicitly) provided
in the output. We manually evaluated up to 30 mappings and we focused the assessment
on unique equivalence mappings.
For example DiSMatch’s output contains 291 unique mappings in the HP-MP task.
The manual assessment revealed an (estimated) precision of 0.8333. In order to also take
into account the number of unique mappings that a system is able to discover, Tables 16
and 17 also include the positive and negative contribution of the unique mappings with
respect to the total unique mappings discovered by all participating systems.
8 MultiFarm
The MultiFarm data set [32] aims at evaluating the ability of matching systems to deal
with ontologies in different natural languages. This data set results from the translation
of 7 ontologies from the conference track (cmt, conference, confOf, iasted, sigkdd,
ekaw and edas) into 10 languages: Arabic, Chinese, Czech, Dutch, French, German,
Italian, Portuguese, Russian, and Spanish. It is composed of 55 pairs of languages (see
[32] for details on how the original MultiFarm data set was generated). For each pair,
taking into account the alignment direction (cmten –confOfde and cmtde –confOfen , for
instance, as two distinct matching tasks), we have 49 matching tasks. The whole data
set is composed of 55 × 49 matching tasks.
8.1 Experimental setting
Part of the data set is used for blind evaluation. This subset includes all matching tasks
involving the edas and ekaw ontologies (resulting in 55 × 24 matching tasks). This
year, we have conducted a minimalistic evaluation and focused on the blind data set.
Participants were able to test their systems on the available subset of matching tasks
(open evaluation), available via the SEALS repository. The open subset covers 45 × 25
tasks. The open subset does not include Italian translations.
We distinguish two types of matching tasks: (i) those tasks where two different
ontologies (cmt–confOf, for instance) have been translated into two different languages;
and (ii) those tasks where the same ontology (cmt–cmt) has been translated into two
different languages. For the tasks of type (ii), good results are not directly related to the
use of specific techniques for dealing with cross-lingual ontologies, but on the ability
to exploit the identical structure of the ontologies.
For the sake of simplicity, we refer in the following to cross-lingual systems those
implementing cross-lingual matching strategies and non-cross-lingual systems those
without that feature.
This year, there were on 7 cross-lingual systems (out of 21): AML, CroLOM-Lite,
IOMAP (renamed SimCat-Lite), LogMap, LPHOM, LYAM++, and XMap. Among these
systems, only CroLOM-Lite and SimCat-Lite are specifically designed to this task. The
reader can refer to the OAEI papers for a detailed description of the strategies adopted
by each system.
The number of participants in fact increased with respect to the last campaign (5 in
2015, 3 in 2014, 7 in 2013, and 7 in 2012).
Following the OAEI evaluation rules, all systems should be evaluated in all tracks
although it is expected that some system produce bad or no results. For this track, we
observed different behaviours:
– CroMatcher and LYAM have experimented internal errors but were able to generated
alignments for less than half of the tasks;
– Alin and Lily have generated no errors but empty alignments for all tasks;
– DKP-AOM and DKP-AOM-Lite were executed without errors but generated align-
ments for less than half of the tasks;
– NAISC has mostly generated erroneous correspondences (for very few tasks) and
RiMOM has basically generated correspondences between ontology annotations;
– Dedicated systems (FCA-Map, LogMapBio, PhenoMF, PhenoMM and PhenoMP)
required more than 30 minutes (in average) for completing a single task and were
not evaluated;
In the following, we report the results for the systems dedicated to the task or that
have been able to provide non-empty alignments for some tasks. We count on 12 sys-
tems (out of 21 participants).
8.2 Execution setting and runtime
The systems have been executed on a Ubuntu Linux machine configured with 8GB of
RAM running under a Intel Core CPU 2.00GHz x4 processors. All measurements are
based on a single run. As Table 18, we can observe large differences in the time required
for a system to complete the 55 x 24 matching tasks. Note as well that the concurrent
access to the SEALS repositories during the evaluation period may have an impact in
the time required for completing the tasks.
8.3 Evaluation results
Table 18 presents the aggregated results for the matching tasks involving edas and ekaw
ontologies. They have been computed using the Alignment API 4.6 and can slightly
differ from those computed with the SEALS client. We haven’t applied any threshold
on the generated alignments. They are measured in terms of classical precision and
recall (future evaluations should include weighted and semantic metrics).
For both types of tasks, most systems favor precision to the detriment of recall.
The exception is LPHOM that has generated huge sets of correspondences (together
with LYAM). As expected, (most) systems cross-lingual systems outperform the non-
cross-lingual ones (the exceptions are LPHOM, LYAM and XMap, which have low per-
formance for different reasons, i.e., many internal exceptions or poor ability to deal
with the specifics of the task). On the other hand, this year, many non-cross-lingual
systems dealing with matching at schema level have been executed with errors (Cro-
Matcher, GA4OM) or were not able to deal at with the tasks (Alin, Lily, NAISC). Hence,
their structural strategies could not be in fact evaluated (tasks of type ii). For both tasks,
DKP-AOM and DKP-AOM-Lite have good performance in terms of precision but gener-
ating few correspondences for less than half of the matching tasks.
In particular, for the tasks of type (i), AML outperforms all other systems in terms
of F-measure, followed by LogMap, CroLOM-Lite and SimCat-Lite. However, LogMap
outperforms all systems in terms of precision, keeping a relatively good performance
in terms of recall. For tasks of type (ii), AML decreases in performance with LogMap
keeping its good results and outperforming all systems, followed by CroLOM-Lite and
SimCat-Lite.
With respect to the pairs of languages for test cases of type (i), for the sake of
brevity, we do not present them here. The reader can refer to the OAEI results web page
for detailed results for each of the 55 pairs. While non-cross-lingual systems were not
able to deal with many pairs of languages (in particular those involving the ar, cn, and
ru languages), only 4 cross-lingual systems were able to deal with all pairs of languages
Type (i) – 22 tests per pair Type (ii) – 2 tests per pair
System Time #pairs Size Prec. F-m. Rec. Size Prec. F-m. Rec.
AML 102 55 13.45 .51(.51) .45(.45) .40(.40) 39.99 .92(.92) .31(.31) .19(.19)
CroLOM-Lite 5501 55 8.56 .55(.55) .36(.36) .28(.28) 38.76 .89(.90) .40(.40) .26(.26)
LogMap 166 55 7.27 .71(.71) .37(.37) .26(.26) 52.81 .96(.96) .44(.44) .30(.30)
LPHOM 2497 34 84.22 .01(.02) .02(.04) .08(.08) 127.91 .13(.22) .13(.21) .13(.13)
LYAM 1367 24 177.30 .01(.00) .006(.01) .00(.00) 283.95 .03(.07) .02(.07) .03(.03)
SimCat-Lite 3938 54 7.07 .59(.60) .34(.35) .25(.25) 30.11 .90(.93) .33(.34) .21(.21)
XMap 134 31 3.93 .30(.54) .01(.01) .00(.00) .00 .00(.00) .00(.00) .00(.00)
CroMatcher 65 25 2.91 .29(.64) .004(.01) .00(.00) .00 .00(.00) .00(.00) .00(.00)
DKP-AOM 34 24 2.58 .42(.98) .03(.08) .02(.02) 4.37 .49(1.0) .01(.03) .01(.07)
DKP-AOM-Lite 35 24 2.58 .42(.98) .03(.08) .02(.02) 4.37 .49(1.0) .01(.03) .01(.01)
LogMapLite 21 55 1.16 .35(.35) .04(.09) .02(.02) 94.50 .01(.01) .01(.01) .01(.01)
NAISC 905 55 1.94 .00(.00) .00(.01) .00(.00) 1.84 .01(.01) .00(.01) .00(.01)
Table 18. MultiFarm aggregated results per matcher, for each type of matching task—different
ontologies (i) and same ontologies (ii). Time is measured in minutes (for completing the 55 × 24
matching tasks). #pairs indicates the number of pairs of languages for which the tool is able to
generated (non empty) alignments. Size indicates the average of the number of generated corre-
spondences for the tests where an (non empty) alignment has been generated. Two kinds of results
are reported: those do not distinguishing empty and erroneous (or not generated) alignments and
those—indicated between parenthesis—considering only non empty generated alignments for a
pair of languages.
(AML, CroLOM-Lite, LogMap and SimCat-Lite). LPHOM has particularly experimented
problems with the pairs involving cn and cz.
Non-cross-lingual systems take advantage of the similarities in the lexicon of some
languages, in the absence of specific strategies. This can be corroborated by the fact
that most of them generate their best F-measure for the pairs es-pt (followed by de-en,
fr-pt and it-pt). This (expected) fact has been observed along the campaigns. Another
previously observed behaviour is related to the fact that, although it is likely harder to
find correspondences between cz-pt than es-pt, for some non-cross-lingual systems this
pair is present in their top F-measure.
Comparison with previous campaigns. The number of cross-lingual participants in-
creased this year with respect to the last 2 campaigns (7 in 2016, 5 in 2015, 3 in 2014,
7 in 2013 and 2012 and 3 in 2011.5). This year, 4 systems have also participated last
year (AML, LogMap, LYAM, and XMap) and we count on 3 new systems (CroLOM-Lite,
LPHOM, SimCat-Lite).
Comparing the results from last year, in terms F-measure and with respect to the
blind evaluation (cases of type i), AML slightly decreases its performance (.45 in 2016
and .47 in 2015). LogMap (and LogMap-Lite maintained its performance (.37), with
XMap decreasing considerably in terms of recall but largely improving its execution
time. Newcomers, specifically dedicated to the task, (CroLOM-Lite) and (SimCat-Lite)
obtained F-measure near to (LogMap).
With respect to non-cross-lingual systems, last year CroMatcher finished the task
without errors what explains its better performance, while DKP-AOM keeped the same
results.
8.4 Conclusion
From 21 participants, half of them have been evaluated in this track. While some cross-
lingual systems were not able to fully deal with the difficulties of the task, some others
were not able to complete many tests due to internal errors, what is also the case for
some non-cross-lingual systems.
In terms of performance, the F-measure for blind tests remains relatively stable
across campaigns. AML and LogMap keep their positions with respect to the previous
campaigns, followed this year by the new systems CroLOM-Lite and SimCat-Lite. Still,
all systems privilege precision to the detriment of recall.
As expected, systems implementing specific methods for dealing with ontologies in
different languages outperform non specific systems. Still, cross-lingual approaches are
mainly based on translation strategies and the combination of other resources (such as
cross-lingual links in Wikipedia or BabelNet) and strategies (machine learning, indirect
alignment composition) remains underexploited. For most systems, the strategy consists
of integrating one translation step before the matching itself.
Finally, this year, a minimalistic evaluation has been conducted (results have not
been reported for the open data set). Furthermore, systems should also be evaluated
using weighted and semantic measures. Multilingual tasks should also be considered
and compared against cross-lingual settings.
9 Interactive matching
The interactive matching track was organised at OAEI 2016 for the fourth time. The
goal of this evaluation is to simulate interactive matching [34,13], where a human ex-
pert is involved to validate correspondences found by the matching system. In the eval-
uation, we look at how interacting with the user improves the matching results. Further,
we look at how the results of the matching systems are influenced when the experts
make mistakes. Currently, this track does not evaluate the user experience nor the user
interfaces of the systems [23].
9.1 Data sets
In this edition, we expanded the Interactive track and used data sets from four other
OAEI tracks: Anatomy (Section 4), Conference (Section 5), LargeBio (Section 6), and
Phenotype (Section 7). For details on the data sets, please refer to their respective sec-
tions.
9.2 Experimental setting
The Interactive track relies on the SEALS client’s oracle class to simulate user interac-
tions. An interactive matching system can present a correspondence to the oracle, which
will tell the system whether that correspondence is correct or wrong. This year we have
extended this functionality by allowing a user to present a collection of mappings si-
multaneously to the oracle.
To simulate the possibility of user errors, the oracle can be set to reply with a given
error probability (randomly, from a uniform distribution). We evaluated systems with
four different error rates: 0.0 (perfect oracle), 0.1, 0.2, and 0.3.
The evaluations of the Conference and Anatomy data sets were run on a server with
3.46 GHz (6 cores) and 8GB RAM allocated to the matching systems. Each system was
run ten times and the final result of a system for each error rate represents the average of
these runs. This is the same configuration which was used in the non-interactive version
of the Anatomy track and runtimes in the interactive version of this track are therefore
directly comparable. For the Conference data set with the ra1 alignment, we considered
macro-average of precision and recall of different ontology pairs, while the number of
interactions represent the total number of interactions in all tasks. Finally, the ten runs
are averaged.
The Phenotype and LargeBio evaluation was run on a Ubuntu Laptop with an Intel
Core i7-4600U CPU @ 2.10GHz x 4 and allocating 15Gb of RAM. Each system was
run only once due to the time required to run some of the systems. Since errors are
randomly introduced we expect minor variations between runs.
9.3 Evaluation
The results are presented for each data set separately: Tables 19-22 and Figure 5 for the
Anatomy data set, Tables 23-26 and Figure 6 for the Conference data set, Tables 27-30
and Figures 7-8 for the Disease and Phenotype data set12 , and Tables 35-42 and Figures
-10 for the LargeBio data set13 .
For the tables we present the following information (column names in parentheses).
– The running time of the systems (Time) in seconds.
– The number of unsatisfiable classes resulting from the alignments computed as
detailed in Section 6 - only for the LargeBio data set.
– The performance of the systems is measured using Precision (Prec.), Recall (Rec.)
and F-measure (F-m.) with respect to the fixed reference alignment. For the
Anatomy track we also present Recall+ (Rec.+) as in Section 4.
– To be able to compare the systems with and without interaction we also provide
the performance results from the original tracks in Precision non-interactive (Prec.
non), Recall non-interactive (Rec. non), F-measure non-interactive (F-m. non) and
Recall+ non-interactive (Rec.+ non). For the ease of reading the tables this infor-
mation is duplicated for each table.
– When the oracle makes mistakes, the oracle uses essentially a modified reference
alignment. The performance of the system with respect to this modified reference
alignment is given in Precision oracle (Prec. oracle), Recall oracle (Rec. oracle)
and F-measure oracle (F-m. oracle). We note that for a perfect oracle these values
are the same as the Precision (Prec.), Recall (Rec.) and F-measure (F-m.) values,
respectively.
– Total requests (Tot Reqs.) represents the number of distinct user interactions with
the tool, where each interaction can contain one or more correspondences that could
be analysed simultaneously.
12
Alin could not complete any of the Phenotype tasks, while XMap did not request any user
interaction in the HP-MP data set and thus only participated de facto in the DOID-ORDO data
set.
13
We have used only the small FMA-NCI and SNOMED-NCI matching tasks of the LargeBio
track (see Section 6) for interactive evaluation. Alin could only complete the small FMA-NCI
task.
Fig. 5. Time intervals between requests to the user/oracle for the Anatomy data set (whiskers:
Q1-1,5IQR, Q3+1,5IQR, IQR=Q3-Q1). The labels under the system names show the average
number of requests and the mean time between the requests for the ten runs.
– In distinct mappings (Dist. Mapps) the mappings that are not conflicting are
counted individually; and if more than three mappings are given, they are all
counted independently, regardless of whether they are conflicting.
– We provide the true positives (TP), true negatives (TN), false positives (FP) and
false negatives (FN) regarding the distinct mapping requests.
– Finally, we provide the performance of the oracle in positive precision (Pos. Prec.)
and negative precision (Neg. Prec.). These are the fraction of positive, repectively,
negative answers given by the oracle that are correct. We note that for a perfect
oracle these values are always equal to 1.
The figures show the time intervals between the questions to the user/oracle for the
different systems and error rates. Different runs are depicted with different colours.
9.4 Discussion
In this paper we provide our general observations and lessons learned. For more details
we refer to the OAEI 2016 web site.
The different systems use different strategies for using the oracle. While LogMap,
XMap and AML make use of user interactions exclusively in the post-matching steps
to filter their candidate mappings, Alin can also add new candidate mappings to its ini-
Prec. Rec. F-m. Rec.+ Prec. Rec. F-m. Tot. Dist. Pos. Neg.
Tool Time Prec. Rec. F-m. Rec.+ non non non Non. oracle oracle oracle Reqs. Mapps TP TN FP FN Prec. Prec.
Alin 505 0.99 0.75 0.85 0.35 0.98 0.34 0.50 0.00 0.99 0.75 0.85 803 1221 626 594 0 0 1.00 1.00
AML 48 0.97 0.95 0.96 0.86 0.95 0.94 0.94 0.83 0.97 0.95 0.96 241 240 51 189 0 0 1.00 1.00
LogMap 27 0.98 0.85 0.91 0.60 0.91 0.85 0.88 0.59 0.98 0.85 0.91 590 590 287 303 0 0 1.00 1.00
XMap 49 0.93 0.87 0.90 0.65 0.93 0.87 0.90 0.65 0.93 0.87 0.90 35 35 5 30 0 0 1.00 1.00
Table 19. Anatomy data set–perfect oracle
Prec. Rec. F-m. Rec.+ Prec. Rec. F-m. Tot. Dist. Pos. Neg.
Tool Time Prec. Rec. F-m. Rec.+ non non non Non. oracle oracle oracle Reqs. Mapps TP TN FP FN Prec. Prec.
Alin 489 0.95 0.70 0.81 0.31 0.98 0.34 0.50 0.00 0.99 0.74 0.85 769 1123 554 451 51 67 0.92 0.87
AML 50 0.95 0.95 0.95 0.86 0.95 0.94 0.94 0.83 0.97 0.95 0.96 273 272 47 194 23 6 0.67 0.97
LogMap 24 0.96 0.83 0.89 0.57 0.91 0.85 0.88 0.59 0.96 0.83 0.89 612 612 258 290 35 28 0.88 0.91
XMap 46 0.93 0.87 0.90 0.65 0.93 0.87 0.90 0.65 0.93 0.87 0.90 35 35 4.2 27.5 2.7 0.8 0.61 0.97
Table 20. Anatomy data set–error rate 0.1
Prec. Rec. F-m. Rec.+ Prec. Rec. F-m. Tot. Dist. Pos. Neg.
Tool Time Prec. Rec. F-m. Rec.+ non non non Non. oracle oracle oracle Reqs. Mapps TP TN FP FN Prec. Prec.
Alin 481 0.91 0.66 0.77 0.27 0.98 0.34 0.50 0.00 0.99 0.74 0.85 750 1077 493 368 94 121 0.84 0.75
AML 48 0.94 0.94 0.94 0.85 0.95 0.94 0.94 0.83 0.97 0.95 0.96 300 299 46 193 46 13 0.50 0.94
LogMap 24 0.94 0.82 0.88 0.54 0.91 0.85 0.88 0.59 0.94 0.81 0.87 645 645 225 287 70 61 0.76 0.82
XMap 47 0.93 0.87 0.90 0.65 0.93 0.87 0.90 0.65 0.93 0.87 0.90 35 35 4.3 25.1 5.4 0.7 0.45 0.97
Table 21. Anatomy data set–error rate 0.2
Prec. Rec. F-m. Rec.+ Prec. Rec. F-m. Tot. Dist. Pos. Neg.
Tool Time Prec. Rec. F-m. Rec.+ non non non Non. oracle oracle oracle Reqs. Mapps TP TN FP FN Prec. Prec.
Alin 472 0.87 0.62 0.72 0.23 0.98 0.34 0.50 0.00 0.99 0.73 0.84 740 1058 430 311 134 182 0.76 0.63
AML 47 0.93 0.94 0.93 0.84 0.95 0.94 0.94 0.83 0.97 0.95 0.96 308 307 40 177 71 18 0.36 0.91
LogMap 24 0.93 0.82 0.87 0.54 0.91 0.85 0.88 0.59 0.93 0.80 0.86 650 650 202 256 106 84 0.66 0.75
XMap 47 0.93 0.87 0.90 0.65 0.93 0.87 0.90 0.65 0.93 0.86 0.90 35 35 3.1 21.8 8.9 1.9 0.27 0.92
Table 22. Anatomy data set–error rate 0.3
Prec. Rec. F-m. Prec. Rec. F-m. Tot. Dist. Pos. Neg.
Tool Time Prec. Rec. F-m. non non non oracle oracle oracle Reqs. Mapps. TP TN FP FN Prec. Prec.
Alin 101 0.96 0.74 0.83 0.89 0.26 0.40 0.96 0.74 0.83 326 574 144 429 0 0 1.00 1.00
AML 29 0.91 0.71 0.80 0.84 0.66 0.74 0.91 0.71 0.80 271 270 47 223 0 0 1.00 1.00
LogMap 26 0.89 0.61 0.72 0.82 0.59 0.69 0.89 0.61 0.72 142 142 49 93 0 0 1.00 1.00
XMap 21 0.84 0.57 0.68 0.84 0.57 0.68 0.84 0.57 0.68 4 4 0 4 0 0 0.00 1.00
Table 23. Conference data set–perfect oracle
Prec. Rec. F-m. Prec. Rec. F-m. Tot. Dist. Pos. Neg.
Tool Time Prec. Rec. F-m. non non non oracle oracle oracle Reqs. Mapps. TP TN FP FN Prec. Prec.
Alin 101 0.79 0.67 0.73 0.89 0.26 0.40 0.96 0.74 0.84 315 557 124 375 42 15 0.75 0.96
AML 30 0.85 0.70 0.77 0.84 0.66 0.74 0.92 0.73 0.82 285 279 51 204 18 5 0.74 0.98
LogMap 26 0.85 0.60 0.70 0.82 0.59 0.69 0.86 0.59 0.70 140 140 45 81 10 3 0.82 0.97
XMap 22 0.84 0.57 0.68 0.84 0.57 0.68 0.84 0.57 0.68 4 4 0 3.6 0.4 0 0.00 1.00
Table 24. Conference data set–error rate 0.1
Prec. Rec. F-m. Prec. Rec. F-m. Tot. Dist. Pos. Neg.
Tool Time Prec. Rec. F-m. non non non oracle oracle oracle Reqs. Mapps. TP TN FP FN Prec. Prec.
Alin 100 0.67 0.62 0.64 0.89 0.26 0.40 0.96 0.75 0.84 303 538 108 321 81 27 0.57 0.92
AML 33 0.77 0.68 0.72 0.84 0.66 0.74 0.93 0.75 0.83 290 277 53 170 42 11 0.56 0.94
LogMap 26 0.82 0.59 0.69 0.82 0.59 0.69 0.83 0.58 0.68 143 143 38 75 18 10 0.68 0.88
XMap 21 0.84 0.57 0.68 0.84 0.57 0.68 0.84 0.57 0.68 4 4 0 3.2 0.8 0 0.00 1.00
Table 25. Conference data set–error rate 0.2
Prec. Rec. F-m. Prec. Rec. F-m. Tot. Dist. Pos. Neg.
Tool Time Prec. Rec. F-m. non non non oracle oracle oracle Reqs. Mapps. TP TN FP FN Prec. Prec.
Alin 99 0.57 0.57 0.57 0.89 0.26 0.40 0.97 0.77 0.86 303 535 93 279 120 42 0.44 0.87
AML 30 0.72 0.65 0.68 0.84 0.66 0.74 0.93 0.75 0.83 284 269 47 143 58 20 0.45 0.88
LogMap 26 0.80 0.59 0.68 0.82 0.59 0.69 0.80 0.56 0.66 144 144 33 67 28 15 0.54 0.82
XMap 22 0.84 0.57 0.68 0.84 0.57 0.68 0.84 0.57 0.68 4 4 0 2.9 1.1 0 0.00 1.00
Table 26. Conference data set–error rate 0.3
Prec. Rec. F-m. Prec. Rec. F-m. Tot. Dist. Pos. Neg.
Tool Time Prec. Rec. F-m. non non non oracle oracle oracle Reqs. Mapps. TP TN FP FN Prec. Prec.
AML 308 0.945 0.899 0.921 0.9 0.851 0.875 0.945 0.899 0.921 388 388 192 196 0 0 1 1
LogMap 329 0.96 0.96 0.96 0.755 0.957 0.844 0.96 0.96 0.96 1,928 1,928 551 1,377 0 0 1 1
Table 27. Phenotype: HP-MP data set–perfect oracle
Prec. Rec. F-m. Prec. Rec. F-m. Tot. Dist. Pos. Neg.
Tool Time Prec. Rec. F-m. non non non oracle oracle oracle Reqs. Mapps. TP TN FP FN Prec. Prec.
AML 306 0.932 0.886 0.908 0.9 0.851 0.875 0.945 0.899 0.921 388 388 171 176 20 21 0.895 0.893
LogMap 346 0.888 0.932 0.909 0.755 0.957 0.844 0.912 0.912 0.912 1,891 1,891 498 1,208 132 53 0.79 0.958
Table 28. Phenotype: HP-MP data set–error rate 0.1
Prec. Rec. F-m. Prec. Rec. F-m. Tot. Dist. Pos. Neg.
Tool Time Prec. Rec. F-m. non non non oracle oracle oracle Reqs. Mapps. TP TN FP FN Prec. Prec.
AML 309 0.923 0.868 0.895 0.9 0.851 0.875 0.944 0.894 0.918 358 358 144 140 32 42 0.818 0.769
LogMap 367 0.836 0.915 0.874 0.755 0.957 0.844 0.871 0.871 0.871 1,855 1,855 440 1,042 262 111 0.627 0.904
Table 29. Phenotype: HP-MP data set–error rate 0.2
Prec. Rec. F-m. Prec. Rec. F-m. Tot. Dist. Pos. Neg.
Tool Time Prec. Rec. F-m. non non non oracle oracle oracle Reqs. Mapps. TP TN FP FN Prec. Prec.
AML 299 0.905 0.856 0.88 0.9 0.851 0.875 0.944 0.899 0.921 390 390 124 138 58 70 0.681 0.663
LogMap 263 0.796 0.907 0.848 0.755 0.957 0.844 0.83 0.83 0.83 1,827 1,827 387 892 384 164 0.502 0.845
Table 30. Phenotype: HP-MP data set–error rate 0.3
Prec. Rec. F-m. Prec. Rec. F-m. Tot. Dist. Pos. Neg.
Tool Time Prec. Rec. F-m. non non non oracle oracle oracle Reqs. Mapps. TP TN FP FN Prec. Prec.
AML 525 0.929 0.993 0.96 0.824 0.99 0.899 0.929 0.993 0.96 413 413 115 298 0 0 1 1
LogMap 440 0.994 0.972 0.983 0.904 0.932 0.918 0.994 0.972 0.983 1,602 1,602 780 822 0 0 1 1
XMap 2,352 0.933 0.714 0.809 0.977 0.622 0.76 0.933 0.714 0.809 11 11 3 8 0 0 1 1
Table 31. Phenotype: DOID-ORDO data set–perfect oracle
Prec. Rec. F-m. Prec. Rec. F-m. Tot. Dist. Pos. Neg.
Tool Time Prec. Rec. F-m. non non non oracle oracle oracle Reqs. Mapps. TP TN FP FN Prec. Prec.
AML 507 0.912 0.988 0.948 0.824 0.99 0.899 0.93 0.992 0.96 413 413 108 266 32 7 0.771 0.974
LogMap 492 0.949 0.927 0.938 0.904 0.932 0.918 0.961 0.939 0.95 1,677 1,677 698 815 82 82 0.895 0.909
XMap 2,603 0.931 0.713 0.808 0.977 0.622 0.76 0.932 0.713 0.808 11 11 3 7 1 0 0.75 1
Table 32. Phenotype: DOID-ORDO data set–error rate 0.1
Prec. Rec. F-m. Prec. Rec. F-m. Tot. Dist. Pos. Neg.
Tool Time Prec. Rec. F-m. non non non oracle oracle oracle Reqs. Mapps. TP TN FP FN Prec. Prec.
AML 513 0.899 0.979 0.937 0.824 0.99 0.899 0.931 0.992 0.961 413 413 94 242 56 21 0.627 0.92
LogMap 428 0.906 0.91 0.908 0.904 0.932 0.918 0.913 0.892 0.902 1,699 1,699 621 716 203 159 0.754 0.818
XMap 2,302 0.931 0.712 0.807 0.977 0.622 0.76 0.932 0.713 0.808 11 11 1 7 1 2 0.5 0.778
Table 33. Phenotype: DOID-ORDO data set–error rate 0.2
Prec. Rec. F-m. Prec. Rec. F-m. Tot. Dist. Pos. Neg.
Tool Time Prec. Rec. F-m. non non non oracle oracle oracle Reqs. Mapps. TP TN FP FN Prec. Prec.
AML 540 0.881 0.975 0.926 0.824 0.99 0.899 0.933 0.992 0.962 413 413 88 204 93 28 0.486 0.879
LogMap 427 0.883 0.904 0.893 0.904 0.932 0.918 0.864 0.845 0.854 1,760 1,760 555 681 299 225 0.65 0.752
XMap 2,260 0.931 0.712 0.807 0.977 0.622 0.76 0.932 0.713 0.808 11 11 1 7 1 2 0.5 0.778
Table 34. Phenotype: DOID-ORDO data set–error rate 0.3
Prec. Rec. F-m. Prec. Rec. F-m. Tot. Dist. Pos. Neg.
Tool Time Unsat. Prec. Rec. F-m. non non non oracle oracle oracle Reqs. Mapps. TP TN FP FN Prec. Prec.
Alin 5,859 2 0.996 0.63 0.772 0.995 0.455 0.624 0.996 0.63 0.772 653 1,019 470 549 0 0 1 1
AML 60 2 0.99 0.913 0.95 0.963 0.902 0.932 0.99 0.913 0.95 449 447 217 230 0 0 1 1
LogMap 38 2 0.992 0.901 0.944 0.944 0.897 0.92 0.992 0.901 0.944 1,131 1,131 594 537 0 0 1 1
XMap 50 2 0.991 0.9 0.943 0.977 0.901 0.937 0.991 0.9 0.943 188 188 114 74 0 0 1 1
Table 35. LargeBio: FMA-NCI small data set–perfect oracle
Prec. Rec. F-m. Prec. Rec. F-m. Tot. Dist. Pos. Neg.
Tool Time Unsat. Prec. Rec. F-m. non non non oracle oracle oracle Reqs. Mapps. TP TN FP FN Prec. Prec.
Alin 5,616 85 0.971 0.614 0.752 0.995 0.455 0.624 0.996 0.63 0.772 629 932 426 420 43 43 0.908 0.907
AML 61 222 0.98 0.908 0.943 0.963 0.902 0.932 0.99 0.914 0.95 497 484 224 219 26 15 0.896 0.936
LogMap 39 2 0.98 0.881 0.928 0.944 0.897 0.92 0.983 0.892 0.935 1,209 1,209 536 582 33 58 0.942 0.909
XMap 51 2 0.988 0.895 0.939 0.977 0.901 0.937 0.99 0.9 0.943 187 187 100 68 4 15 0.962 0.819
Table 36. LargeBio: FMA-NCI small data set–error rate 0.1
Prec. Rec. F-m. Prec. Rec. F-m. Tot. Dist. Pos. Neg.
Tool Time Unsat. Prec. Rec. F-m. non non non oracle oracle oracle Reqs. Mapps. TP TN FP FN Prec. Prec.
Alin 5,398 152 0.958 0.593 0.733 0.995 0.455 0.624 0.996 0.624 0.767 605 881 370 353 63 95 0.855 0.788
AML 63 2 0.974 0.894 0.932 0.963 0.902 0.932 0.987 0.91 0.947 450 450 166 185 43 56 0.794 0.768
LogMap 38 2 0.967 0.874 0.918 0.944 0.897 0.92 0.964 0.875 0.917 1,247 1,247 488 558 95 106 0.837 0.84
XMap 58 2 0.988 0.892 0.938 0.977 0.901 0.937 0.99 0.899 0.942 187 187 92 67 6 22 0.939 0.753
Table 37. LargeBio: FMA-NCI small data set–error rate 0.2
Prec. Rec. F-m. Prec. Rec. F-m. Tot. Dist. Pos. Neg.
Tool Time Unsat. Prec. Rec. F-m. non non non oracle oracle oracle Reqs. Mapps. TP TN FP FN Prec. Prec.
Alin 5,347 91 0.937 0.58 0.716 0.995 0.455 0.624 0.996 0.623 0.767 589 855 335 293 99 128 0.772 0.696
AML 63 2 0.966 0.894 0.929 0.963 0.902 0.932 0.981 0.911 0.945 450 450 160 174 53 63 0.751 0.734
LogMap 39 2 0.963 0.872 0.915 0.944 0.897 0.92 0.935 0.849 0.89 1,327 1,327 429 572 161 165 0.727 0.776
XMap 53 2 0.985 0.887 0.933 0.977 0.901 0.937 0.99 0.899 0.942 188 188 80 59 14 35 0.851 0.628
Table 38. LargeBio: FMA-NCI small data set–error rate 0.3
Prec. Rec. F-m. Prec. Rec. F-m. Tot. Dist. Pos. Neg.
Tool Time Unsat. Prec. Rec. F-m. non non non oracle oracle oracle Reqs. Mapps. TP TN FP FN Prec. Prec.
AML 730 0 0.972 0.726 0.831 0.904 0.713 0.797 0.972 0.726 0.831 2,730 2,730 1,657 1,073 0 0 1 1
LogMap 628 0 0.985 0.669 0.797 0.922 0.663 0.771 0.985 0.669 0.797 5,596 5,596 3,742 1,854 0 0 1 1
XMap 984 35,869 0.924 0.59 0.72 0.911 0.564 0.697 0.924 0.59 0.72 11,932 11,689 10,090 1,599 0 0 1 1
Table 39. LargeBio: SNOMED-NCI small data set–perfect oracle
Prec. Rec. F-m. Prec. Rec. F-m. Tot. Dist. Pos. Neg.
Tool Time Unsat. Prec. Rec. F-m. non non non oracle oracle oracle Reqs. Mapps. TP TN FP FN Prec. Prec.
AML 759 0 0.967 0.717 0.823 0.904 0.713 0.797 0.972 0.724 0.83 2,730 2,730 1,495 979 92 164 0.942 0.857
LogMap 619 16 0.974 0.651 0.78 0.922 0.663 0.771 0.971 0.656 0.783 6,201 6,201 3,357 2,263 196 385 0.945 0.855
XMap 957 35,455 0.923 0.591 0.721 0.911 0.564 0.697 0.84 0.568 0.678 11,931 11,694 9,095 1,512 89 998 0.99 0.602
Table 40. LargeBio: SNOMED-NCI small data set–error rate 0.1
Prec. Rec. F-m. Prec. Rec. F-m. Tot. Dist. Pos. Neg.
Tool Time Unsat. Prec. Rec. F-m. non non non oracle oracle oracle Reqs. Mapps. TP TN FP FN Prec. Prec.
AML 762 0 0.961 0.707 0.815 0.904 0.713 0.797 0.972 0.721 0.828 2,730 2,730 1,331 891 181 327 0.88 0.732
LogMap 625 16 0.965 0.64 0.77 0.922 0.663 0.771 0.948 0.639 0.763 6,737 6,737 2,977 2,505 490 765 0.859 0.766
XMap 943 35,968 0.921 0.591 0.72 0.911 0.564 0.697 0.754 0.541 0.63 11,911 11,682 8,052 1,403 204 2023 0.975 0.41
Table 41. LargeBio: SNOMED-NCI small data set–error rate 0.2
Prec. Rec. F-m. Prec. Rec. F-m. Tot. Dist. Pos. Neg.
Tool Time Unsat. Prec. Rec. F-m. non non non oracle oracle oracle Reqs. Mapps. TP TN FP FN Prec. Prec.
AML 758 0 0.955 0.697 0.806 0.904 0.713 0.797 0.972 0.719 0.827 2,730 2,730 1,184 798 264 484 0.818 0.622
LogMap 635 16 0.959 0.635 0.764 0.922 0.663 0.771 0.92 0.62 0.741 7,159 7,159 2,607 2,563 854 1,135 0.753 0.693
XMap 984 36,619 0.919 0.592 0.72 0.911 0.564 0.697 0.676 0.514 0.584 11,903 11,693 7,090 1,266 347 2,990 0.953 0.297
Table 42. LargeBio: SNOMED-NCI small data set–error rate 0.3
Fig. 6. Average time between requests per task in the Conference data set (whiskers: Q1-1,5IQR,
Q3+1,5IQR, IQR=Q3-Q1). The labels under the system names show the average number of re-
quests and the mean time between the requests (calculated by taking the average of the average
request intervals per task) for the ten runs and all tasks.
tial set. LogMap and AML both request feedback on only selected mapping candidates
(based on their similarity patterns or their involvement in unsatisfiabilities) and only
present one mapping at a time to the user. XMap also presents one mapping at a time
and asks mainly for true negatives. Only Alin employs the new feature in this year’s
evaluation: analysing several conflicting mappings simultaneously, whereby a system
can present up to three mappings together to the oracle, provided that each mapping
presented has a mapped entity, i.e., class or property, in common with at least one other
mapping presented.
The performance of the systems improves when interacting with a perfect oracle
compared to no interaction. Although systems’ performance deteriorates when moving
towards larger error rates there are still benefits from the user interaction—some of the
systems’ measures stay above their non-interactive values even for the larger error rates.
For the Anatomy track Alin detects only trivial correspondences in the non-interactive
version while user interactions led to detecting some non-trivial correspondences.
The impact of the oracle’s errors is linear for Alin, AML and XMap and supra-linear
for LogMap for all data sets. The ”Positive Precision” value affects the true positives
and false positives, and the ”Negative Precision” value affects the true negatives and
Fig. 7. Time between requests per task in the HP-MP data set (whiskers: Q1-1,5IQR, Q3+1,5IQR,
IQR=Q3-Q1). The labels under the system names show the number of requests and the mean time
between the requests.
false negatives. The more a system relies on the oracle, the more sensitive it will be to
its errors.
In general, XMap performs very few requests to the oracle compared to the other
systems.
Two models for system response times are frequently used in the literature [10]:
Shneiderman and Seow take different approaches to categorise the response times.
Shneiderman takes a task-centred view and sorts the response times in four categories
according to task complexity: typing, mouse movement (50-150 ms), simple frequent
tasks (1 s), common tasks (2-4 s) and complex tasks (8-12 s). He suggests that the user
is more tolerable to delays with the growing complexity of the task at hand. Unfortu-
nately, no clear definition is given for how to define the task complexity. Seow’s model
looks at the problem from a user-centred perspective by considering the user expec-
tations towards the execution of a task: instantaneous (100-200 ms), immediate (0.5-1
s), continuous (2-5 s), captive (7-10 s); Ontology alignment is a cognitively demanding
task and can fall into the third or fourth categories in both models. In this regard the
response times (request intervals as we call them above) observed in all data sets fall
into the tolerable and acceptable response times, and even into the first categories, in
both models. The request intervals for both AML and LogMap stay under 3 ms for all
Fig. 8. Time between requests per task in the DOID-ORDO data set (whiskers: Q1-1,5IQR,
Q3+1,5IQR, IQR=Q3-Q1). The labels under the system names show the number of requests
and the mean time between the requests.
data sets. Alin’s request intervals are higher, but still in the tenth of second range. It
could be the case however that the user could not take advantage of very low response
times because the task complexity may result in higher user response time (analogically
it measures the time the user needs to respond to the system after the system is ready).
Regarding the number of unsatisfiable classes resulting from the alignments we
observe some expected variations as the error increases. We note that, with interaction,
the alignments produced by the systems are typically larger than without interaction,
which makes the repair process harder. The introduction of oracle errors complicates
the process further, and may make an alignment irreparable if the system follows the
oracle’s feedback blindly.
10 Instance matching
The instance matching track aims at evaluating the performance of matching tools
when the goal is to detect the degree of similarity between pairs of items/instances
expressed in the form of RDF data. The track is organized in three independent tasks
called SABINE, SYNTHETIC and DOREMUS. Each test is based on two data sets called
source and target and the goal is to discover the matching pairs, i.e., mappings, among
the instances in the source data set and the instances in the target data set.
Fig. 9. Time between requests per task in the FMA-NCI data set (whiskers: Q1-1,5IQR,
Q3+1,5IQR, IQR=Q3-Q1). The labels under the system names show the number of requests
and the mean time between the requests.
For the sake of clarity, we split the presentation of task results in three different
sections as follows.
10.1 Results of the SABINE task
SABINE is a modular benchmark in the domain of European politics for Social Busi-
ness Intelligence (SBI) and it includes an ontology with 500 topics, both in English and
Italian languages. The task is articulated in two sub-tasks called inter-lingual mapping
and data linking.
In inter-lingual mapping, source and target datasets are OWL ontologies containing
topics as instances of the class “Topic”. The source ontology contains topics in the
English language; the target ontology contains other topics in the Italian language. The
goal is to discover mappings between English and Italian topics by also defining the
kind of relation which is most suitable for describing the discovered mapping between
two matching topics.
In data linking, just the source dataset is defined and it is given to the participants
as an OWL ontology containing topics as instances of the class “Topic”. The goal is to
discover the best corresponding DBpedia entity for each topic in the source ontology.
Fig. 10. Time between requests per task in the SNOMED-NCI data set (whiskers: Q1-1,5IQR,
Q3+1,5IQR, IQR=Q3-Q1). The labels under the system names show the number of requests and
the mean time between the requests.
The SABINE sub-tasks are defined as open tests, meaning that the set of expected
mappings, i.e., reference alignment, is given in advance to the participants and it con-
stitutes the gold standard for result evaluation. The task size is around 23K ontology
instances to consider. The gold standard has been defined through crowdsourcing vali-
dation though the Argo system14 . For creating the gold standard, workers are called to
recognize and confirm the mapping between instance topics of source and target on-
tologies. In particular, a task is represented as a choice question in which a topic of the
source ontology is specified and a number of instance topics of the target ontology are
provided as possible mappings. A worker receiving a task to execute has to consider
a source topic and to choose the most appropriate mapping with a target topic among
those provided as possible options. Multi-worker task assignment and consensus evalu-
ation techniques are defined in Argo for quality assessment of the task result. A task is
assigned to a group G of 6 different workers. A group member autonomously executes
a task and independently produces the answer according to her/his personal feeling and
judgement. Given a task, its result is defined as an answer agreement, i.e., consensus,
among the members of the group that executed the task. Two workers agree on a task
result when they selected the same target topic as mapping with the given source topic.
14
http://island.ricerca.di.unimi.it/projects/argo/ (in Italian).
The mapping between the source and the target topics is confirmed and inserted in the
gold standard when the task answer having the highest degree of consensus within the
group G is supported by a qualified majority larger than 50%. Conversely, when a qual-
ified majority of workers is not found in G, the task is uncommitted and it is scheduled
for re-execution by a different group of workers with higher reliability. Further details
on the Argo techniques for task management are provided in [7]. The gold standard of
the SABINE task contains 249 crowd-validated mappings for the inter-lingual sub-task
and 338 crowd-validated mappings for the data linking sub-task.
Participants to the SABINE sub-tasks are LogMapIm, AML, LogMapLite, and Ri-
MOM. Results are shown in Table 43. For each test, the tool performances are expressed
in terms of precision, recall, and F-measure.
Inter-lingual mapping Data linkinging
Precision F-measure Recall Precision F-measure Recall
LogMapIm 0.012 0.014 0.016 NaN NaN 0.0
AML 0.919 0.917 0.916 0.926 0.889 0.855
LogMapLite 0.358 0.214 0.153 NaN NaN 0.0
RiMOM 0.955 0.943 0.932 0.424 0.580 0.917
Table 43. Instance matching results.
We focus our considerations on AML and RiMOM that provided high-value results
for precision, recall, and F-measure on both inter-lingual mapping and data linking sub-
tasks. In particular, RiMOM outperforms AML on the inter-lingual mapping sub-task, in
that both precision and recall values of RiMOM are higher than the corresponding values
of AML. However, both tools are over 90% for precision and recall values, meaning
that mapping corresponding instances of different languages is a successfully-addressed
task by RiMOM and AML. In the data linking sub-task, AML outperforms RiMOM on
precision and the difference between the tools on this result value is significant (i.e.,
AML >90% and RiMOM <50% on the precision value). On the opposite, for recall, we
note that the RiMOM value is higher than the AML value, and the result of both tools
is very positive (i.e., >85%). We argue that these results on the data linking sub-task
are due to the problem of selecting the most appropriate mapping when a number of
possible alternatives are available. Both AML and RiMOM are successful in providing a
set of candidate DBpedia entities as target mapping with a given OWL instance (i.e.,
high recall value). On the opposite, the capability to choose/select the most appropriate
mapping among the set of available options is still challenging and only AML succeeds
in providing high-quality results on this task (i.e., high precision value).
10.2 Results of the SYNTHETIC task
UOBM and SPIMBENCH tasks are two of the evaluation tasks of instance matching
tools where the goal is to determine when two OWL instances describe the same real
world object. For the first task, the data sets have been produced by altering a set of
source data and generated by SPIMBENCH [37] with the aim to generate descriptions
of the same entity where value-based, structure-based and semantics-aware transforma-
tions are employed in order to create the target data. While, for the latter task the data
sets have been generated with the University Ontology Benchmark (UOBM) [30] and
transformed with the LANCE benchmark generator [36].
For both tasks, the transformations applied were a combination of value-based,
structure-based, and semantics-aware test cases. The value-based transformations con-
sider mainly typographical errors and different data formats, the structure-based trans-
formations consider transformations applied on the structure of object and datatype
properties and the semantics-aware transformations are transformations at the instance
level considering the TBox information. The latter are used to examine if the matching
systems take into account RDFS and OWL semantics in order to discover correspon-
dences between instances that can be found only by considering information found in
the TBox.
We stress that an instance in the source data set can have none or one matching
counterpart in the target data set. A data set is composed of a TBox and a corresponding
ABox. Source and target data sets share almost the same TBox (differences in the prop-
erties, due to the structure-based transformations). For SPIMBENCH, the sandbox scale
is 10K triples ≈380 instances while the mainbox scale is 50K triples ≈1800 instances.
We asked the participants to match the Creative Works instances (NewsItem, BlogPost
and Programme) in the source data set against the instances of the corresponding class
in the target data set. For UOBM, the sandbox scale is 14K triples ≈2.5K instances
while the mainbox scale is 60K triples ≈10K instances. We asked the participants to
match all the instances that are not common to the two data sets. For both tasks, we ex-
pected to receive a set of links denoting the pairs of matching instances that they found
to refer to the same entity.
The participants to these tasks are LogMap, AML and RiMOM. For evaluation, we
built a ground truth containing the set of expected links where an instance i1 in the
source data set is associated with an instance in the target data set that has been gener-
ated as an altered description of i1 .
The way that the transformations were done, was to apply value-based, structure-
based and semantics-aware transformations, on different triples pertaining to one class
instance.
The systems were judged on the basis of precision, recall and F-measure results that
are shown in Tables 44 and 45.
Sandbox task Mainbox task
Precision F-measure Recall Precision F-measure Recall
LogMap 0.958 0.851 0.766 0.981 0.814 0.695
AML 0.907 0.82 0.749 0.9 0.816 0.747
RiMOM 0.984 0.992 1 0.991 0.995 1
Table 44. Results of the SPIMBENCH task.
LogMap responds well regarding the SPIMBENCH task, while the performance
drops when matching the data sets of the UOBM task. LogMap is automatic and does
not require the definition of a configuration file in contrast to AML and RiMOM.
Sandbox task Mainbox task
Precision F-measure Recall Precision F-measure Recall
LogMap 0.701 0.32 0.207 0.625 0.044 0.023
AML 0.785 0.665 0.577 0.509 0.512 0.515
RiMOM 0.771 0.821 0.877 0.443 0.477 0.516
Table 45. Results of the UOBM task.
AML responds well regarding the SPIMBENCH task, while the performance drops
when matching the data sets of the UOBM task. AML had to turn off the reasoner in
order to handle missing information about the domain and range of TBox properties.
LogMap and AML produce links that are quite often correct (resulting in a good
precision) but fail in capturing a large number of the expected links (resulting in a
lower recall).
RiMOM performs better than any other system for most of the tasks; it performs
excellent in the case of SPIMBENCH but, although it exhibits the best results for the
Sandbox track of UOBM, its performance drops for the Mainbox track. For RiMOM,
the probability of capturing a correct link is high, but the probability of a retrieved link
to be correct is lower, resulting in a high recall but not a high precision.
The main comments for the SPIMBENCH and UOBM tasks are:
– LogMap and AML have consistent behaviour regarding Sandbox and Mainbox.
– RiMOM has a consistent behaviour for the SPIMBENCH task and an inconsistent
behaviour for the UOBM task.
– All systems performed well for the SPIMBENCH task.
– The UOBM data sets seem to be more “difficult” for both IM systems, and this dif-
ficulty stems from the data set itself, rather than from the transformations imposed
by LANCE.
– The UOBM data sets seem to be more difficult for both IM systems, and this dif-
ficulty stems from the data set itself, rather than from the transformations imposed
by LANCE. In particular, an important source of difficulty for the systems is that
the URIs of the instances in the data set look very similar to each other, so even the
change of a URI can lead to false positives or false negatives.
10.3 Results of the DOREMUS task
The DOREMUS task, having its premier at OAEI, contains real world data sets coming
from two major French cultural institutions—The BnF (French National Library) and
the PP (Philharmonie de Paris). The data are about classical music works and follow the
DOREMUS model (one single vocabulary for both data sets) issued from the DORE-
MUS project15 . Each data entry, or instance, is a bibliographical record about a musical
piece, containing properties such as the composer, the title(s) of the work, the year of
creation, the key, the genre, the instruments, to name a few. These data have been con-
verted to RDF from their original UNI- and INTER-MARC format and anchored to the
DOREMUS ontology and a set of domain controlled vocabularies by the help of the
marc2rdf converter16 , developed for this purpose within the DOREMUS Project (for
15
http://www.doremus.org
16
https://github.com/DOREMUS-ANR/marc2rdf
more details on the conversion method and on the ontology we refer to [1] and [29]).
Note that these data are highly heterogeneous. We have selected works described both
at the BnF and at the PP with different degrees of heterogeneity in their descriptions.
The data sets have been selected in three sub-tasks.
Nine heterogeneities. This task consists in aligning two small data sets, BnF-1 and PP-
1, containing about 40 instances each, by discovering 1:1 equivalence relations between
their instances. There are 9 types of heterogeneities that these data manifest, that have
been identified by the music library experts, such as multilingualism, differences in cat-
alogues, differences in spelling, different degrees of description (number of properties).
Four heterogeneities. This task consists in aligning two larger data sets, BnF-2 and
PP-2, containing about 200 instances each, by discovering 1:1 equivalence relations
between the instances that they contain. There are 4 types of heterogeneities that these
data manifest, that we have selected from the nine in Task 1 and that appear to be
the most problematic: 1) Orthographical differences, 2) Multilingual titles, 3) Missing
properties, 4) Missing titles.
The False Positives Trap. This task consists in correctly disambiguating the instances
contained in two data sets, BnF-3 and PP-3, by discovering 1:1 equivalence relations
between the instances that they contain. We have selected several groups of pairs of
works with highly similar descriptions where there exists only one correct match in
each group. The goal is to challenge the linking tools capacity to avoid the generation
of false positives and match correctly instances in the presence of highly similar but
still distinct candidates.
9 heterogeneities 4 heterogeneities False positive trap
Prec. F-m. Rec. Prec. F-m. Rec. Prec. F-m. Rec.
AML (th=0.2) 0.966 0.918 0.875 0.934 0.848 0.776 0.921 0.886 0.854
AML (th=0.6) 0.962 0.862 0.781 0.943 0.83 0.741 0.853 0.773 0.707
RiMOM 0.813 0.813 0.813 0.746 0.746 0.746 0.707 0.707 0.707
Table 46. Results of the DOREMUS task
Results Only two systems returned results on the track: AML and RiMOM. Note that
AML has been configured with two different thresholds. The results of their perfor-
mances, evaluated by using precision, recall and F-measure, on each of the three tasks
can be seen in Table 46. The best performance in terms of F-measure is provided by the
AML tool with a threshold of 0.2 on all tasks.
11 Process Model Matching
In 2013 and in 2015 the community interested in business process modelling conducted
an evaluation campaign similar to OAEI [3]. Instead of matching ontologies, the task
was to match process models described in different formalisms like BPMN and Petri
Nets. Within this track we offer a subset of the tasks from the Process Model Matching
Contest as OAEI track by converting the process models to an ontological represen-
tation. By offering this track, we hope to gain insights in how far ontology matching
systems are capable of solving the more specific problem of matching process mod-
els. This track is also motivated by the discussions at the end of the 2015 Ontology
Matching workshop, where many participants showed their interest in such a track.
11.1 Experimental Settings
We were using the first data set from the 2015 Process Matching Contest. This data set
deals with processing applications to a university. It consists of nine different process
models where each describes the concrete process of a specific German university. The
models are encoded as BPMN process models. We converted the BPMN representa-
tion of the process models to a set of assertions (ABox) using the vocabulary defined
in the BPMN 2.0 ontology (TBox). For that reason the resulting matching task is an
instance matching task where each ABox is described by the same TBox. For each
pair of processes manually generated reference alignments are available. Typical activ-
ities within that domain are Sending acceptance, Invite student for interview, or Wait
for response. These examples illustrate one of the main differences from the ontology
matching task. The labels are usually verb-object phrases that are sometimes extended
with more words. Another important difference is related to the existence of an execu-
tion order, i.e., the model is a complex sequence of activities, which can be understood
as the counterpart to a type hierarchy.
Only few systems have been marked as capable of generating alignments for the
Process Model Matching track. We have tried to execute all these systems, however,
some of them generated only trivial TBox mappings instead of mappings between ac-
tivities. After contacting the developer of the systems, we received the feedback that
the systems have been marked mistakenly and are designed for terminological match-
ing only. We have excluded them from the evaluation. Moreover, we tried to run all
systems that were marked as instance matching tools, which have been submitted as
executable SEALS bundles. One of these tools (LogMap), generated meaningful results
and was also added to the set of systems that we evaluated. Finally we evaluated three
systems (AML, LogMap, and DKP), one of these systems was configured in two different
settings related to the treatment of events-to-activity mappings. This was the tool DKP.
Thus we distinguish between DKP and DKP*.
In our evaluation, we computed standard precision and recall, as well as the har-
monic mean known as F-measure. The data set we used consists of several test cases.
We aggregated the results and present the micro average results. The gold standard we
used for our first set of evaluation experiments is based on the gold standard that has
also been used at the Process Model Matching Contest in 2015 [3]. We modified only
some minor mistakes (resulting in changes less than 0.5 percentage points). In order to
compare the results to the results obtained by the process model matching community,
we present also the recomputed values of the submissions to the 2015 contest.
Moreover, we extended our evaluation (“Standard” in Table 47) by a new evalua-
tion measure that makes use of a probabilistic reference alignment (“Probabilistic” in
Table 47). This probabilistic measure is based on a gold standard which is manually
and independently generated by several domain experts. The number of votes of these
annotators are applied as support values in the probabilistic evaluation. For a detailed
discussion, please refer to [28].
11.2 Results
Table 47 summarises the results of our evaluation. “P” abbreviates precision, “R” is
recall, “FM” stands for F-measure and “Rk” means rank. The prefix “Pro” indicates the
probabilistic versions of the precision, recall, F-measure and the associated rank. These
metrics are explained below. Participants of the Process Model Matching Contest in
2015 (PMMC 2015) are depicted in grey font, while OAEI 2016 participants are shown
in black font. The OAEI participants are ranked on position 1, 8, 9 and 11 with an overall
number of 16 systems listed in the table (when using the standard metrics). Note that
AML-PM at the PMMC 2015 was a matching system that was based on a predecessor
of AML participating at OAEI 2016. The good results of AML are surprising, since we
expected that matching systems specifically developed for the purpose of process model
matching would outperform ontology matching systems applied to the special case of
process model matching. While AML contains also components that are specifically
designed for the process matching task (a flooding-like structural matching algorithm),
its relevant main components are components developed for ontology matching and the
sub-problem of instance matching.
Participants Standard Probabilistic
Matcher Contest Size P R FM Rk ProP ProR ProFM Rk
AML OAEI-16 221 0,719 0,685 0,702 1 0,742 0,283 0,410 2
AML-PM PMMC-15 579 0,269 0,672 0,385 14 0,377 0,398 0,387 4
BPLangMatch PMMC-15 277 0,368 0,440 0,401 12 0,532 0,272 0,360 8
DKP OAEI-16 177 0,621 0,474 0,538 8 0,686 0,219 0,333 9
DKP* OAEI-16 150 0,680 0,440 0,534 9 0,772 0,211 0,331 10
KnoMa-Proc PMMC-15 326 0,337 0,474 0,394 13 0,506 0,302 0,378 5
KMatch-SSS PMMC-15 261 0,513 0,578 0,544 6 0,563 0,274 0,368 7
LogMap OAEI-16 267 0,449 0,517 0,481 11 0,594 0,291 0,390 3
Match-SSS PMMC-15 140 0,807 0,487 0,608 4 0,761 0,192 0,307 12
OPBOT PMMC-15 234 0,603 0,608 0,605 5 0,648 0,258 0,369 6
pPalm-DS PMMC-15 828 0,162 0,578 0,253 16 0,210 0,335 0,258 16
RMM-NHCM PMMC-15 220 0,691 0,655 0,673 2 0,783 0,297 0,431 1
RMM-NLM PMMC-15 164 0,768 0,543 0,636 3 0,681 0,197 0,306 13
RMM-SMSL PMMC-15 262 0,511 0,578 0,543 7 0,516 0,242 0,329 11
RMM-VM2 PMMC-15 505 0,216 0,470 0,296 15 0,309 0,294 0,301 14
TripleS PMMC-15 230 0,487 0,483 0,485 10 0,486 0,210 0,293 15
Table 47. Results of the process model matching track
In the probabilistic evaluation, however, the OAEI participants gain position 2, 3, 9
and 10, respectively. LogMap rises from position 11 to 3. The (probabilistic) precision
improves over-proportionally for this matcher, because LogMap generates many corre-
spondences which are not included in the binary gold standard but are included in the
probabilistic one. The ranking of LogMap demonstrates that a strength of the probabilis-
tic metric lies in the broadened definition of the gold standard where weak mappings
are included but softened (via the support values).
Figures 11(a)-(b) show the probabilistic precision (ProP) and the probabilistic re-
call (ProR) with rising threshold τ on the reference alignment (0,000; 0,375; 0,500;
0,750). The matcher LogMap mainly identifies correspondences with high support (of
which many are not included in the binary gold standard). This can be observed by the
minor change in the ProP and the significant increase in the ProR with higher τ . For
the matcher AML, the opposite effect can be observed. The ProP decreases dramatically
with rising τ (accompanied by a weak increase of the ProR). This indicates that the
matcher computes a high fraction of correspondences with low support value (which
are partly included in the binary gold standard). For the matchers DKP and DKP*, with
increasing τ , a minor decrease in ProP and increase in ProR can be observed. The
ProP decreases, since the number of correspondences in the non-binary gold standard
decreases (with rising τ ). At the same time, the ProR increases with a lower number
of correspondences (with rising τ ). Figure 11(c) displays the probabilistic F-measure
(ProFM) with rising threshold τ on the reference alignment. AML achieves best results
with τ = 0,375 since this matcher identifies a high fraction of correspondences with
low support value (which can also be trivial correspondences). For details about the
probabilistic metric, please refer to [28].
The results depicted in Table 47 and Figure 11 indicate that the progress made in
ontology matching has also a positive impact on other related matching problems, like
it is the case for process model matching. While it might require to reconfigure, adapt,
and extend some parts of the ontology matching systems, such a system seems to offer
a good starting point which can be turned with a reasonable amount of work into a good
process matching tool. We have to emphasise that our observations are so far based on
only one data set. Moreover, only three participants decided to apply their systems to
the new track of process model matching. Thus, we have to be cautious to generalise
the results we observed so far. In the future we might be able to attract more participants
integrating more data sets in the evaluation.
12 Lesson learned and suggestions
The lessons learned from running OAEI 2016 were the following:
A) This year, as suggested in previous campaigns, we requested tool registration in
June and preliminary submission of wrapped systems by the end of July. This mea-
sure was successful in reducing the number of systems with errors and incompati-
bilities with the SEALS client during the evaluation phase as had happened in the
past. However, not all systems complied with the deadlines, and some did have
problems, which still delayed the evaluation. In future editions, we must be more
strict in enforcing the participation protocol.
B) Thanks in part to the new submission schedule, this marked the first OAEI edition
where all participants and all tracks were evaluated using the SEALS client. Nev-
ertheless, some system developers still struggled to get their systems working with
the client, mostly due to incompatible versions of libraries. This recurring problem,
plus the effort required to update the SEALS client’s libraries, lead to the consid-
eration of whether it would not be better to develop a simpler, more streamlined
evaluation solution.
(a) Probabilistic precision
(b) Probabilistic recall
(c) Probabilistic F-measure
Fig. 11. Change in metric values with rising threshold τ .
C) The continued absence of the SEALS web portal did not seem to affect participa-
tion, as the Google drive solution for submission was well received by the partici-
pants. OAEI may move towards a cloud-based solution.
D) While the number of participants this year was similar to that of recent years, their
distribution through the tracks was uneven. Long-standing tracks had no shortage
of participants, but alas the same was not true for the Interactive, Process Model
(new) or Instance (new data sets) tracks. One reason for this is that the OAEI data
sets have been released too close to the submission deadline to allow system devel-
opers to develop their systems to tackle them all—the timing is barely sufficient to
allow serious development focusing on one new data set. Thus, with prize money
on offer on one of the new tracks, it is no surprise that system developers were
polarised towards that track and eschewed the other new ones. We should consider
anticipating the deadline for initial release of OAEI data sets, particular for those
that are new, in order to give system developers more time to tackle them, thereby
increasing participation.
E) The increasing variety of OAEI tracks also poses difficulties to system developers
in configuring their systems to handle different types of tasks. It is noteworthy that
only two systems, both of which are long-term OAEI participants, have tackled all
tracks—and one of them did so using external configuration files specifying the
type of task. One solution to facilitate participation in multiple tracks would be
to have the evaluation client transmit to the system the specifications of the task,
e.g., whether classes, properties, and/or individuals are to be matched, and whether
only a specific subset of them are to be matched. This would also make the tasks
more realistic, in the sense that in normal use, a user would provide to the ontology
matching system this type of information.
F) With regard to the low participation in the Process Model and Instance tracks, it
merits considering whether enforcing adherence to the SEALS client and ontology-
based data sets were not deterrent factors. It should be noted that the Process Model
Matching Contest (PMMC) received a much larger number of participants in 2015
than did the Process Model track, and that there is a considerable number of pub-
lications on data interlinking systems, but only one of these participated in the
Instance track.
G) In previous years we identified the need for considering non-binary forms of eval-
uation, namely in cases where there is uncertainty about some of the reference
mappings. A first non-binary evaluation type was implemented in last year’s Con-
ference track, but this year two new tracks followed suit: Disease and Phenotype
where the evaluation was semantic, and Process Model, where it was probabilistic.
These new strategies should provide a fairer evaluation of the systems in complex
test cases.
The lessons learned in the various OAEI 2016 track were the following:
largebio: While the current reference alignments, with incoherence-causing mappings
flagged as uncertain, make the evaluation fair to all systems, they are only a com-
promise solution, not an ideal one. Thus, we should aim for manually repairing and
validating the reference alignments for future editions.
phenotype: The prize offered in this track, thanks to the kind sponsorship of the Pistoia
Alliance Ontologies Mapping project, was positively accepted by the community
and helped attract new participants. However, it also had a polarising effect, with
some systems focusing exclusively in this track. In future editions, we will consider
including a prize across OAEI tracks in order to motivate developers to successfully
participate in more than one track.
interactive: The new functionality of the Oracle allowing systems to submit a set of up
to three conflicting mappings, rather than a mapping at a time, was successfully ex-
ploited by one new participating system. Nevertheless, this track’s participation has
remained low, as most systems participating in OAEI focussed exclusively on fully
automatic matching. We hope to draw more participants to this track in the future
and will continue to expand it so as to better approximate real user interactions.
process model: The results of the new Process Model track have shown that the partic-
ipating ontology matching systems are capable of generating very good results for
the specific problem of process model matching. This shows that the basic com-
ponents of an ontology matching system can also be successfully applied to other
kind of matching problems.
instance: In order to attract more instance matching systems to participate in value
semantics (val-sem), value structure (val-struct), and value structure semantics (val-
struct-sem) tasks, we need to produce benchmarks that have fewer instances (in the
order of 10000), of the same type (in our benchmark we asked systems to compare
instances of different types). To balance those aspects, we must then produce data
sets with more complex transformations.
13 Conclusions
OAEI 2016 saw the same number (21) of participants as in recent years, with a healthy
mix of new and returning systems. While some new participants were mainly drawn
by the allure of prize money in the new Disease and Phenotype track, the very fact
that there was prize money on offer shows that interest in ontology matching is not
waning, which bodes well for the future of OAEI. All the test cases were performed
on the SEALS client, including those in the instance matching track, which is good
news regarding the interoperability of matching systems. Furthermore, the fact that the
SEALS client can be used for such a variety of tasks is a good sign of its relevance.
Unlike previous years, this year there was no noticeable improvement with regard
to system run times—for instance, the distribution of run times in Anatomy and Large
Biomedical Ontologies was approximately the same as last year. There was also no
progress with regard to the ability to handle large ontologies and data sets, as the number
of systems able to cope with the Large Biomedical Ontologies data set in full was the
same as last year, and all systems able to cope with the Instance Synthetic data set were
established systems already known for their ability to handle large data sets. Finally,
there was no progress with regard to alignment repair systems, with only a few returning
systems employing them. As a consequence, incoherent alignments are common.
With regard to F-measure, some returning systems showed substantial improve-
ments, but overall, the improvements in F-measure were subtle in Anatomy and Large
Biomedical Ontologies, and non-existent in Conference. As has been the trend, most
systems favour precision over recall.
Most of the participants have provided a description of their systems and their ex-
perience in the evaluation. These OAEI papers, like the present one, have not been peer
reviewed. However, they are full contributions to this evaluation exercise and reflect
the hard work and clever insight people put into the development of participating sys-
tems. Reading the papers of the participants should help people involved in ontology
matching find out what makes these algorithms work and what could be improved.
The Ontology Alignment Evaluation Initiative will strive to continue to be a ref-
erence to the ontology matching community by improving both the test cases and the
testing methodology to better reflect the actual needs of the community. Evaluating on-
tology matching systems remains a challenging but critical topic, which is essential to
enable the progress of this field [38]. More information can be found at:
http://oaei.ontologymatching.org.
Acknowledgements
We warmly thank the participants of this campaign. We know that they have worked hard to have
their matching tools executable in time and they provided useful reports on their experience. The
best way to learn about the results remains to read the papers that follow.
We would also like to thank the Pistoia Alliance9 which sponsored the Disease and Phenotype
track and funded the prize for the winners.
We are very grateful to the Universidad Politécnica de Madrid (UPM), especially to Nan-
dana Mihindukulasooriya and Asunción Gómez Pérez, for moving, setting up and providing the
necessary infrastructure to run the SEALS repositories.
We are also grateful to Martin Ringwald and Terry Hayamizu for providing the reference
alignment for the anatomy ontologies and thank Elena Beisswanger for her thorough support on
improving the quality of the data set.
We thank Khiat Abderrahmane for his support in the Arabic data set and Catherine Comparot
for her feedback and support in the MultiFarm test case.
We also thank for their support the other members of the Ontology Alignment Evaluation Ini-
tiative steering committee: Yannis Kalfoglou (Ricoh laboratories, UK), Miklos Nagy (The Open
University (UK), Natasha Noy (Stanford University, USA), Yuzhong Qu (Southeast University,
CN), York Sure (Leibniz Gemeinschaft, DE), Jie Tang (Tsinghua University, CN), George Vouros
(University of the Aegean, GR).
Michelle Cheatham has been supported by the National Science Foundation award ICER-
1440202 “EarthCube Building Blocks: Collaborative Proposal: GeoLink”.
Jérôme Euzenat, Ernesto Jimenez-Ruiz, Christian Meilicke, Heiner Stuckenschmidt and
Cássia Trojahn dos Santos have been partially supported by the SEALS (IST-2009-238975) Eu-
ropean project in previous years.
Daniel Faria was supported by the ELIXIR-EXCELERATE project (INFRADEV-3-2015).
Ernesto Jimenez-Ruiz has also been partially supported by the Seventh Framework Program
(FP7) of the European Commission under Grant Agreement 318338, “Optique”, the EPSRC
projects DBOnto and ED3, the Research Council of Norway project BigMed, and the Centre
for Scalable Data Access (SIRIUS).
Catia Pesquita was supported by the FCT through the LASIGE Strategic Project
(UID/CEC/00408/2013) and the research grant PTDC/EEI-ESS/4633/2014.
Ondřej Zamazal has been supported by the CSF grant no. 14-14076P.
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