=Paper=
{{Paper
|id=Vol-2288/oaei18_paper0
|storemode=property
|title=Results of the Ontology Alignment Evaluation Initiative 2018
|pdfUrl=https://ceur-ws.org/Vol-2288/oaei18_paper0.pdf
|volume=Vol-2288
|authors=Alsayed Algergawy,Michelle Cheatham,Daniel Faria,Alfio Ferrara,Irini Fundulaki,Ian Harrow,Sven Hertling,Ernesto Jiménez-Ruiz,Naouel Karam,Abderrahmane Khiat,Patrick Lambrix,Huanyu Li,Stefano Montanelli,Heiko Paulheim,Catia Pesquita,Tzanina Saveta,Daniela Schmidt,Pavel Shvaiko,Andrea Splendiani,Elodie Thiéblin,Cássia Trojahn,Jana Vataščinová,Ondřej Zamazal,Lu Zhou
|dblpUrl=https://dblp.org/rec/conf/semweb/AlgergawyCFFFHH18
}}
==Results of the Ontology Alignment Evaluation Initiative 2018==
https://ceur-ws.org/Vol-2288/oaei18_paper0.pdf
Results of the
Ontology Alignment Evaluation Initiative 2018?
Alsayed Algergawy1 , Michelle Cheatham2 , Daniel Faria3 , Alfio Ferrara4 ,
Irini Fundulaki5 , Ian Harrow6 , Sven Hertling7 , Ernesto Jiménez-Ruiz8,9 ,
Naouel Karam10 , Abderrahmane Khiat11 , Patrick Lambrix12 , Huanyu Li12 ,
Stefano Montanelli4 , Heiko Paulheim7 , Catia Pesquita13 , Tzanina Saveta5 ,
Daniela Schmidt14 , Pavel Shvaiko15 , Andrea Splendiani6 , Elodie Thiéblin16 ,
Cássia Trojahn16 , Jana Vataščinová17 , Ondřej Zamazal17 , and Lu Zhou2
1
Friedrich Schiller University Jena, Germany
alsayed.algergawy@uni-jena.de
2
Data Semantics (DaSe) Laboratory, Wright State University, USA
{michelle.cheatham, zhou.34}@wright.edu
3
Instituto Gulbenkian de Ciência, Lisbon, Portugal
dfaria@igc.gulbenkian.pt
4
Università degli studi di Milano, Italy
{alfio.ferrara,stefano.montanelli}@unimi.it
5
Institute of Computer Science-FORTH, Heraklion, Greece
{jsaveta,fundul}@ics.forth.gr
6
Pistoia Alliance Inc., USA
{ian.harrow,andrea.splendiani}@pistoiaalliance.org
7
University of Mannheim, Germany
{sven,heiko}@informatik.uni-mannheim.de
8
Department of Informatics, University of Oslo, Norway
ernestoj@ifi.uio.no
9
The Alan Turing Institute, London, UK
ejimenez-ruiz@turing.ac.uk
10
Fraunhofer FOKUS, Berlin, Germany
naouel.karam@fokus.fraunhofer.de
11
Freie Universität Berlin, Germany
abderrahmane.khiat@fu-berlin.de
12
Linköping University & Swedish e-Science Research Center, Linköping, Sweden
{patrick.lambrix,huanyu.li}@liu.se
13
LASIGE, Faculdade de Ciências, Universidade de Lisboa, Portugal
cpesquita@di.fc.ul.pt
14
Pontifical Catholic University of Rio Grande do Sul, Brazil
daniela.schmidt@acad.pucrs.br
15
TasLab, Trentino Digitale SpA, Trento, Italy
pavel.shvaiko@tndigit.it
16
IRIT & Université Toulouse II, Toulouse, France
{cassia.trojahn,elodie.thieblin}@irit.fr
17
University of Economics, Prague, Czech Republic
ondrej.zamazal@vse.cz
?
Note that the only official results of the campaign are on the OAEI web site.
Abstract. The Ontology Alignment Evaluation Initiative (OAEI) aims at com-
paring ontology matching systems on precisely defined test cases. These test
cases can be based on ontologies of different levels of complexity (from simple
thesauri to expressive OWL ontologies) and use different evaluation modalities
(e.g., blind evaluation, open evaluation, or consensus). The OAEI 2018 campaign
offered 12 tracks with 23 test cases, and was attended by 19 participants. This
paper is an overall presentation of that campaign.
1 Introduction
The Ontology Alignment Evaluation Initiative1 (OAEI) is a coordinated international
initiative, which organizes the evaluation of an increasing number of ontology matching
systems [18, 20]. The main goal of the OAEI 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,
developers can improve their systems.
Two first events were organized in 2004: (i) the Information Interpretation and In-
tegration Conference (I3CON) held at the NIST Performance Metrics for Intelligent
Systems (PerMIS) workshop and (ii) the Ontology Alignment Contest held at the Eval-
uation of Ontology-based Tools (EON) workshop of the annual International Semantic
Web Conference (ISWC) [45]. Then, a unique OAEI campaign occurred in 2005 at the
workshop on Integrating Ontologies held in conjunction with the International Con-
ference on Knowledge Capture (K-Cap) [4]. From 2006 until the present, the OAEI
campaigns were held at the Ontology Matching workshop, collocated with ISWC [1–3,
6–8, 11, 14–17, 19], which this year took place in Monterey, CA, USA2 .
Since 2011, we have been using an environment for automatically processing eval-
uations (§2.1) which was developed within the SEALS (Semantic Evaluation At Large
Scale) project3 . SEALS provided a software infrastructure for automatically executing
evaluations and evaluation campaigns for typical semantic web tools, including ontol-
ogy matching. Since OAEI 2017, a novel evaluation environment called HOBBIT (§2.1)
was adopted for the HOBBIT Link Discovery track, and later extended to enable the
evaluation of other tracks. Some tracks are run exclusively through SEALS and others
through HOBBIT, but several allow participants to choose the platform they prefer.
This paper synthesizes the 2018 evaluation campaign and introduces the results
provided in the papers of the participants. The remainder of the paper is organized as
follows: in §2, we present the overall evaluation methodology; in §3 we present the
tracks and datasets; in §4 we present and discuss the results; and finally, §5 concludes
the paper.
1
http://oaei.ontologymatching.org
2
http://om2018.ontologymatching.org
3
http://www.seals-project.eu
2 Methodology
2.1 Evaluation platforms
The OAEI evaluation was carried out in one of two alternative platforms: the SEALS
client or the HOBBIT platform. Both have the goal of ensuring reproducibility and
comparability of the results across matching systems.
The SEALS client was developed in 2011. It is a Java-based command line inter-
face for ontology matching evaluation, which requires system developers 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 is provided to the participants, de-
scribing how to wrap a tool and how to run a full evaluation locally.
The HOBBIT platform4 was introduced in 2017. It is a web interface for linked
data and ontology matching evaluation, which requires systems to be wrapped inside
docker containers and include a SystemAdapter class, then being uploaded into the
HOBBIT platform [31].
Both platforms compute the standard evaluation metrics against the reference align-
ments: precision, recall and F-measure. In test cases where different evaluation modali-
ties are required, evaluation was carried out a posteriori, using the alignments produced
by the matching systems.
2.2 OAEI campaign phases
As in previous years, the OAEI 2018 campaign was divided into three phases: prepara-
tory, execution, and evaluation.
In the preparatory phase, the test cases were provided to participants in an initial
assessment period between June 15th and July 15th , 2018. The goal of this phase is to
ensure that the test cases make sense to participants, and give them the opportunity to
provide feedback to organizers on the test case as well as potentially report errors. At
the end of this phase, the final test base was frozen and released.
During the ensuing execution phase, participants test and potentially develop their
matching systems to automatically match the test cases. Participants can self-evaluate
their results either by comparing their output with the reference alignments or by using
either of the evaluation platforms. They can tune their systems with respect to the non-
blind evaluation as long as they respect the rules of the OAEI. Participants were required
to register their systems and make a preliminary evaluation by July 31st . The execution
phase was terminated on September 9th , 2018, at which date participants had to submit
the (near) final versions of their systems (SEALS-wrapped and/or HOBBIT-wrapped).
During the evaluation phase, systems were evaluated by all track organizers. In
case minor problems were found during the initial stages of this phase, they were re-
ported to developers, who were given the opportunity to fix and resubmit their systems.
Initial results were provided directly to the participants, whereas final results for most
tracks were published on the respective pages of the OAEI website by October 8th .
4
https://project-hobbit.eu/outcomes/hobbit-platform/
3 Tracks and test cases
This year’s OAEI campaign consisted of 12 tracks gathering 23 test cases, all of which
were based on OWL ontologies. They can be grouped into:
– Schema matching tracks, which have as objective matching ontology classes and/or
properties.
– Instance Matching tracks, which have as objective matching ontology instances.
– Instance and Schema Matching tracks, which involve both of the above.
– Complex Matching tracks, which have as objective finding complex correspon-
dences between ontology entities.
– Interactive tracks, which simulate user interaction to enable the benchmarking of
interactive matching algorithms.
The tracks are summarized in Table 1.
Table 1. Characteristics of the OAEI tracks.
Test Cases
Track Relations Confidence Evaluation Languages Platform
(Tasks)
Schema Matching
Anatomy 1 = [0 1] open EN SEALS
Biodiversity
2 = [0 1] open EN SEALS
& Ecology
Conference 1 (21) =, <= [0 1] open+blind EN SEALS
Disease &
2 =, <= [0 1] open+blind EN SEALS
Phenotype
Large Biomedical
6 = [0 1] open EN both
ontologies
AR, CZ, CN,
DE, EN, ES,
Multifarm 2 (2695) = [0 1] open+blind SEALS
FR, IT, NL,
RU, PT
Instance Matching
IIMB 1 = [0 1] open+blind EN SEALS
Link Discovery 2 (9) = [0 1] open EN HOBBIT
SPIMBENCH 2 = [0 1] open+blind EN HOBBIT
Instance and Schema Matching
Knowledge Graph 9 = [0 1] open EN both
Interactive Matching
Interactive 2 (22) =, <= [0 1] open EN SEALS
Complex Matching
Complex 4 = [0 1] open+blind EN, ES SEALS
Open evaluation is made with already published reference alignments and blind evaluation is
made by organizers, either from reference alignments unknown to the participants or manually.
3.1 Anatomy
The anatomy track comprises a single test case consisting of matching two fragments
of biomedical ontologies which describe the human anatomy5 (3304 classes) and the
anatomy of the mouse6 (2744 classes). The evaluation is based on a manually curated
reference alignment. This dataset has been used since 2007 with some improvements
over the years [13].
Systems are evaluated with the standard parameters of precision, recall, F-measure.
Additionally, recall+ is computed by excluding trivial correspondences (i.e., correspon-
dences that have the same normalized label). Alignments are also checked for coher-
ence using the Pellet reasoner. The evaluation was carried out on a server with a 6
core CPU @ 3.46 GHz with 8GB allocated RAM, using the SEALS client. However,
the evaluation parameters were computed a posteriori, after removing from the align-
ments produced by the systems s expressing relations other than equivalence, as well
as trivial correspondences in the oboInOwl namespace (e.g., oboInOwl#Synonym =
oboInOwl#Synonym). The results obtained with the SEALS client vary in some cases
by 0.5% compared to the results presented below.
3.2 Biodiversity and Ecology
The new biodiversity track features two test cases based on highly overlapping ontolo-
gies that are particularly useful for biodiversity and ecology research: matching the
Environment Ontology (ENVO) to the Semantic Web for Earth and Environment Tech-
nology Ontology (SWEET), and matching the Flora Phenotype Ontology (FLOPO)
to the Plant Trait Ontology (PTO). The track was motivated by two projects, namely
GFBio7 (The German Federation for Biological Data) and AquaDiva8 , which aim at
providing semantically enriched data management solutions for data capture, annota-
tion, indexing and search [32]. Table 2 summarizes the versions and the sizes of the
ontologies used in OAEI 2018.
Table 2. Versions and number of classes of the Biodiversity and Ecology track ontologies.
Ontology Version Classes
ENVO 2017-08-22 6909
SWEET 2018-03-12 4543
FLOPO 2016-06-03 24199
PTO 2017-09-11 1504
The reference alignments for the two test cases were produced through a hybrid
approach that consisted of (1) using established matching systems to produce an au-
5
http://www.cancer.gov/cancertopics/cancerlibrary/
terminologyresources/
6
http://www.informatics.jax.org/searches/AMA_form.shtml
7
www.gfbio.org
8
www.aquadiva.uni-jena.de
tomated consensus alignment (akin to those used in the Disease and Phenotype track)
then (2) manually validating the unique results produced by each system (and adding
them to the consensus if deemed correct), and finally (3) adding manually generated
correspondences. The matching systems used were the OAEI 2017 versions of AML,
LogMap, LogMapBio, LogMapLite, LYAM, POMap, and YAMBio, in addition to the
alignments from BioPortal [38].
The evaluation was carried out on a Windows 10 (64-bit) desktop with an Intel Core
i5-7500 CPU @ 3.40GHz x 4 with 15.7 Gb RAM allocated, using the SEALS client.
Systems were evaluated using the standard metrics.
3.3 Conference
The conference track features a single test case that is a suite of 21 matching tasks corre-
sponding to the pairwise combination of 7 moderately expressive ontologies describing
the domain of organizing conferences. The dataset and its usage are described in [47].
The track uses several reference alignments for evaluation: the old (and not fully
complete) manually curated open reference alignment, ra1; an extended, also manu-
ally curated version of this alignment, ra2; a version of the latter corrected to resolve
violations of conservativity, rar2; and an uncertain version of ra1 produced through
crowd-sourcing, where the score of each correspondences is the fraction of people in
the evaluation group that agree with the correspondence. The latter reference was used
in two evaluation modalities: discrete and continuous evaluation. In the former, corre-
spondences in the uncertain reference alignment with a score of at least 0.5 are treated
as correct whereas those with lower score are treated as incorrect, and standard evalu-
ation parameters are used to evaluated systems. In the latter, weighted precision, recall
and F-measure values are computed by taking into consideration the actual scores of
the uncertain reference, as well as the scores generated by the matching system. For
the sharp reference alignments (ra1, ra2 and rar2), the evaluation is based on the stan-
dard parameters, as well the F0.5 -measure and F2 -measure and on conservativity and
consistency violations. Whereas F1 is the harmonic mean of precision and recall where
both receive equal weight, F2 gives higher weight to recall than precision and F0.5 gives
higher weight to precision higher than recall.
Two baseline matchers are use to benchmark the systems: edna string edit distance
matcher; and StringEquiv string equivalence matcher as in the anatomy test case.
The evaluation was carried out on a Windows 10 (64-bit) desktop with an Intel
Core i7–8550U (1,8 GHz, TB 4 GHz) x 4 with 16 GB RAM allocated using the SEALS
client. Systems were evaluated using the standard metrics.
3.4 Disease and Phenotype
The Disease and Phenotype is organized by the Pistoia Alliance Ontologies Mapping
project team9 . It comprises 2 test cases that involve 4 biomedical ontologies cov-
ering the disease and phenotype domains: Human Phenotype Ontology (HP) versus
9
http://www.pistoiaalliance.org/projects/ontologies-mapping/
Mammalian Phenotype Ontology (MP) and Human Disease Ontology (DOID) ver-
sus Orphanet and Rare Diseases Ontology (ORDO). Currently, correspondences be-
tween these ontologies are mostly curated by bioinformatics and disease experts who
would benefit from automation of their workflows supported by implementation of on-
tology matching algorithms. More details about the Pistoia Alliance Ontologies Map-
ping project and the OAEI evaluation are available in [23]. Table 3.4 summarizes the
versions of the ontologies used in OAEI 2018.
Table 3. Disease and Phenotype ontology versions and sources.
Ontology Version Source
HP 2017-06-30 OBO Foundry
MP 2017-06-29 OBO Foundry
DOID 2017-06-13 OBO Foundry
ORDO v2.4 ORPHADATA
The reference alignments used in this track are silver standard consensus alignments
automatically built by merging/voting the outputs of the participating systems in 2016,
2017 and 2018 (with vote=3). Note that systems participating with different variants
and in different years only contributed once in the voting, that is, the voting was done
by family of systems/variants rather than by individual systems. The HP-MP silver
standard thus produced contains 2232 correspondences, whereas the DOID-ORDO one
contains 2808 correspondences.
Systems were evaluated using the standard parameters as well as the number of
unsatisfiable classes computed using the OWL 2 reasoner HermiT [36]. The evaluation
was carried out in a Ubuntu 18 Laptop with an Intel Core i9-8950HK CPU @ 2.90GHz
x 12 and allocating 25 Gb RAM.
3.5 Large Biomedical Ontologies
The large biomedical ontologies (largebio) track aims at finding alignments between
the large and semantically rich biomedical ontologies FMA, SNOMED-CT, and NCI,
which contain 78,989, 306,591 and 66,724 classes, respectively. The track consists of
six test cases corresponding to three matching problems (FMA-NCI, FMA-SNOMED
and SNOMED-NCI) in two modalities: small overlapping fragments and whole ontolo-
gies (FMA and NCI) or large fragments (SNOMED-CT).
The reference alignments used in this track are derived directly from the UMLS
Metathesaurus [5] as detailed in [29], then automatically repaired to ensure logical
coherence. However, rather than use a standard repair procedure of removing prob-
lem causing correspondences, we set the relation of such correspondences to “?” (un-
known). These “?” correspondences are neither considered positive nor negative when
evaluating matching systems, but are simply ignored. This way, systems that do not
perform alignment repair are not penalized for finding correspondences that (despite
causing incoherences) may or may not be correct, and systems that do perform align-
ment repair are not penalized for removing such correspondences. To avoid any bias,
correspondences were considered problem causing if they were selected for removal
by any of the three established repair algorithms: Alcomo [34], LogMap [28], or AML
[39]. The reference alignments are summarized in Table 4.
Table 4. Number of correspondences in the reference alignments of the large biomedical ontolo-
gies tasks.
Reference alignment “=” corresp. “?” corresp.
FMA-NCI 2,686 338
FMA-SNOMED 6,026 2,982
SNOMED-NCI 17,210 1,634
The evaluation was carried out in a Ubuntu 18 Laptop with an Intel Core i9-8950HK
CPU @ 2.90GHz x 12 and allocating 25 Gb of RAM. Evaluation was based on the
standard parameters (modified to account for the “?” relations) as well as the number
of unsatisfiable classes and the ratio of unsatisfiable classes with respect to the size of
the union of the input ontologies. Unsatisfiable classes were computed using the OWL
2 reasoner HermiT [36], or, in the cases in which HermiT could not cope with the input
ontologies and the alignments (in less than 2 hours) a lower bound on the number of
unsatisfiable classes (indicated by ≥) was computed using the OWL 2 EL reasoner ELK
[33].
3.6 Multifarm
The multifarm track [35] aims at evaluating the ability of matching systems to deal with
ontologies in different natural languages. This dataset results from the translation of 7
ontologies from the conference track (cmt, conference, confOf, iasted, sigkdd, ekaw and
edas) into 10 languages: Arabic (ar), Chinese (cn), Czech (cz), Dutch (nl), French (fr),
German (de), Italian (it), Portuguese (pt), Russian (ru), and Spanish (es). The dataset
is composed of 55 pairs of languages, with 49 matching tasks for each of them, taking
into account the alignment direction (e.g. cmten →edasde and cmtde →edasen are dis-
tinct matching tasks). While part of the dataset is openly available, all matching tasks
involving the edas and ekaw ontologies (resulting in 55 × 24 matching tasks) are used
for blind evaluation.
We consider two test cases: i) those tasks where two different ontologies
(cmt→edas, for instance) have been translated into two different languages; and ii)
those tasks where the same ontology (cmt→cmt) has been translated into two differ-
ent languages. For the tasks of type ii), good results are not only related to the use of
specific techniques for dealing with cross-lingual ontologies, but also on the ability to
exploit the identical structure of the ontologies.
The reference alignments used in this track derive directly from the manually cu-
rated Conference ra1 reference alignments. Systems are evaluated using the standard
parameters. The evaluation was carried out on a Ubuntu 16.04 machine configured with
16GB of RAM running under a i7-4790K CPU 4.00GHz x 8 processors, using the
SEALS client.
3.7 IIMB
The new IIMB (ISLab Instance Matching Benchmark) track features a single test case
consisting of 80 instance matching tasks, in which the goal is to match an original
OWL Abox to an automatically transformed version of this Abox using the SWING
(Semantic Web INstance Generation) framework [22]. SWING consists of a pool of
transformation techniques organized as follows:
– Data value transformations (DVL) are based on changes of cardinality and content
of property values belonging to instance descriptions (e.g. value deletion, value
modification through random character insertion/substitution).
– Data structure transformations (DST) are based on changes of property names and
structure within an instance description (e.g. string value splitting, property name
modification).
– Data semantics transformations (DSS) are based on changes of class/type proper-
ties belonging to instance descriptions (e.g. property type deletion/modification).
The IIMB dataset has been generated by relying on a seed of linked-data instances
I 0 extracted from the web. A set of manipulated instances I 00 has been created from I 0
and inserted in IIMB by applying a combination of SWING transformation techniques
according to the following schema:
– Tasks ID 001-020: DVL transformations
– Tasks ID 021-040: DST transformations
– Tasks ID 041-060: DSS transformations
– Tasks ID 061-080: DVL, DST, and DSS transformations
Within a group of tasks, the complexity of applied transformations increases with
the task ID. In each task, the reference alignment corresponds to the correspondence-set
generated by SWING between the instances of the original and transformed Abox.
The evaluation has been performed on an Intel Xeon E5/Core i7 server with 16GB
RAM, the Ubuntu operating systems equipped with the SEALS client.
3.8 Link Discovery
The Link Discovery track features two test cases, Linking and Spatial, that deal with
link discovery for spatial data represented as trajectories i.e., sequences of longi-
tude, latitude pairs. The track is based on two datasets generated from TomTom10 and
Spaten [10].
The Linking test case aims at testing the performance of instance matching tools
that implement mostly string-based approaches for identifying matching entities. It can
be used not only by instance matching tools, but also by SPARQL engines that deal
with query answering over geospatial data. The test case was based on SPIMBENCH
[40], but since the ontologies used to represent trajectories are fairly simple and do
not consider complex RDF or OWL schema constructs already supported by SPIM-
BENCH, only a subset of the transformations implemented by SPIMBENCH was used.
10
https://www.tomtom.com/en_gr/
The transformations implemented in the test case were (I) string-based with different
(a) levels, (b) types of spatial object representations and (c) types of date representa-
tions, and (II) schema-based, i.e., addition and deletion of ontology (schema) properties.
These transformations were implemented in the TomTom dataset. In a nutshell, instance
matching systems are expected to determine whether two traces with their points anno-
tated with place names designate the same trajectory. In order to evaluate the systems
we built a ground truth containing the set of expected links where an instance s1 in
the source dataset is associated with an instance t1 in the target dataset that has been
generated as a modified description of s1 .
The Spatial test case aims at testing the performance of systems that deal with topo-
logical relations proposed in the state of the art DE-9IM (Dimensionally Extended nine-
Intersection Model) model [44]. The benchmark generator behind this test case imple-
ments all topological relations of DE-9IM between trajectories in the two dimensional
space. To the best of our knowledge such a generic benchmark, that takes as input tra-
jectories and checks the performance of linking systems for spatial data does not exist.
For the design, we focused on (a) on the correct implementation of all the topological
relations of the DE-9IM topological model and (b) on producing large datasets large
enough to stress the systems under test. The supported relations are: Equals, Disjoint,
Touches, Contains/Within, Covers/CoveredBy, Intersects, Crosses, Overlaps. The test
case comprises tasks for all the DE-9IM relations and for LineString/LineString and
LineString/Polygon cases, for both TomTom and Spaten datasets, ranging from 200 to
2K instances. We did not exceed 64 KB per instance due to a limitation of the Silk
system11 , in order to enable a fair comparison of the systems participating in this track.
The evaluation for both test cases was carried out using the HOBBIT platform.
3.9 SPIMBENCH
The SPIMBENCH track consists of matching instances that are found to refer to the
same real-world entity corresponding to a creative work (that can be a news item,
blog post or programme). The datasets were generated and transformed using SPIM-
BENCH [40] by altering a set of original linked data through value-based, structure-
based, and semantics-aware transformations (simple combination of transformations).
They share almost the same ontology (with some differences in property level, due
to the structure-based transformations), which describes instances using 22 classes, 31
Data Properties, and 85 Object Properties. Participants are requested to produce a set
of correspondences between the pairs of matching instances from the source and tar-
get datasets that are found to refer to the same real-world entity. An instance in the
source dataset can have none or one matching counterparts in the target dataset. The
SPIMBENCH task is composed of two datasets12 with different scales (i.e., number of
instances to match):
– Sandbox (380 INSTANCES, 10000 TRIPLES). It contains two datasets called
source (Tbox1) and target (Tbox2) as well as the set of expected correspondences
(i.e., reference alignment).
11
https://github.com/silk-framework/silk/issues/57
12
Although the files are called Tbox1 and Tbox2, they actually contain a Tbox and an Abox.
– Mainbox (1800 CWs, 50000 TRIPLES). It contains two datasets called source
(Tbox1) and target (Tbox2). This test case is blind, meaning that the reference
alignment is not given to the participants. In both datasets, the goal is to discover
the correspondences among the instances in the source dataset (Tbox1) and the
instances in the target dataset (Tbox2).
The evaluation was carried out using the HOBBIT platform.
3.10 Knowledge Graph
The new Knowledge Graph track consists of nine isolated graphs generated by running
the DBpedia extraction framework on nine different Wikis from the Fandom Wiki host-
ing platform13 in the course of the DBkWik project [24, 25]. These knowledge graphs
cover three different topics, with three knowledge graphs per topic, so the track consists
of nine test cases, corresponding to the pairwise combination of the knowledge graphs
in each topic. The goal of each test case is to match both the instances and the schema
simultaneously. The datasets are summarized in Table 5
Table 5. Characteristics of the Knowledge Graphs in the KG track, and the sources they were
created from.
Source Hub Topic #Instances #Properties #Classes
RuneScape Wiki Games Gaming 200,605 1,998 106
Old School RuneScape Wiki Games Gaming 38,563 488 53
DarkScape Wiki Games Gaming 19,623 686 65
Marvel Database Comics Comics 56,464 99 2
Hey Kids Comics Wiki Comics Entertainment 158,234 1,925 181
DC Database Comics Lifestyle 128,495 177 5
Memory Alpha TV Entertainment 63,240 326 0
Star Trek Expanded Universe TV Entertainment 17,659 201 3
Memory Beta Books Entertainment 63,223 413 11
The evaluation was based on a gold standard14 of correspondences both on the
schema and the instance level. While the schema level correspondences were created
by experts, the instance correspondences were crowd sourced using Amazon MTurk.
Since we do not have a correspondence for each instance, class, and property in the
graphs, this gold standard is only a partial gold standard.
The evaluation was executed on a virtual machine (VM) with 32GB of RAM and
16 vCPUs (2.4 GHz), with Debian 9 operating system and Openjdk version 1.8.0 181,
using the SEALS client. It was not executed on the HOBBIT platform because few sys-
tems registered in HOBBIT for this task and all of them also had a SEALS counterpart.
We used the -o option in SEALS (version 7.0.5) to provide the two knowledge
graphs which should be matched. We used local files rather than HTTP URLs to cir-
cumvent the overhead of downloading the knowledge graphs. We could not use the
13
https://www.wikia.com/
14
http://dbkwik.webdatacommons.org
”-x” option of SEALS because we had to modify the evaluation routine for two rea-
sons. First, we wanted to differentiate between results for class, property, and instance
correspondences, and second, we had to change the evaluation to deal with the partial
nature of our gold standard.
The alignments were evaluated based on precision, recall, and f-measure for classes,
properties, and instances (each in isolation). Our partial gold standard contained 1:1
correspondences, as well as negative correspondences, i.e., correspondences stating that
a resource A in one knowledge graph has no correspondence in the second knowledge
graph. This allows to increase the count of false positives if the matcher nevertheless
finds a correspondence (i.e., maps A to a resource in the other knowledge graph). We
further assume that in each knowledge graph, only one representation of the concept
exists. This means that if we have a correspondence in our gold standard, we count a
correspondence to a different concept as a false positive. The count of false negatives
is only increased if we have a 1:1 correspondence and it is not found by a matcher. The
whole source code for generating the evaluation results is also available15 .
As a benchmark, we employed a simple string matching approach with some out of
the box text preprocessing to generate a baseline. The source code for this approach is
publicly available16 .
3.11 Interactive Matching
The interactive matching track aims to assess the performance of semi-automated
matching systems by simulating user interaction [37, 12]. The evaluation thus focuses
on how interaction with the user improves the matching results. Currently, this track
does not evaluate the user experience or the user interfaces of the systems [26, 12].
The interactive matching track is based on the datasets from the Anatomy and Con-
ference tracks, which have been previously described. It relies on the SEALS client’s
Oracle class to simulate user interactions. An interactive matching system can present
a collection of correspondences simultaneously to the oracle, which will tell the system
whether that correspondence is correct or not. If a system presents up to three corre-
spondences together and each correspondence presented has a mapped entity (i.e., class
or property) in common with at least one other correspondence presented, the oracle
counts this as a single interaction, under the rationale that this corresponds to a sce-
nario where a user is asked to choose between conflicting candidate correspondences.
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 user), 0.1, 0.2, and 0.3.
In addition to the standard evaluation parameters, we also compute the number of
requests made by the system, the total number of distinct correspondences asked, the
number of positive and negative answers from the oracle, the performance of the system
according to the oracle (to assess the impact of the oracle errors on the system) and
finally, the performance of the oracle itself (to assess how erroneous it was).
15
http://oaei.ontologymatching.org/2018/results/knowledgegraph/
kg_track_eval.zip
16
http://oaei.ontologymatching.org/2018/results/knowledgegraph/
string_baseline_kg-source.zip
The evaluation was carried out 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. For the Conference
dataset with the ra1 alignment, precision and recall correspond to the micro-average
over all ontology pairs, whereas the number of interactions is the total number of inter-
actions for all the pairs.
3.12 Complex Matching
The complex matching track is meant to evaluate the matchers based on their abil-
ity to generate complex alignments. A complex alignment is composed of com-
plex correspondences typically involving more than two ontology entities, such as
o1 :AcceptedPaper ≡ o2 :Paper u o2 :hasDecision.o2 :Acceptance. Four datasets with
their own evaluation process have been proposed [46].
The complex conference dataset is composed of three ontologies: cmt, conference
and ekaw from the conference dataset. The reference alignment was created as a con-
sensus between experts. In the evaluation process, the matchers can take the simple
reference alignment ra1 as input. The precision and recall measures are manually cal-
culated over the complex equivalence correspondences only.
The Hydrography dataset consists of matching four different source ontologies
(hydro3, hydrOntology-translated, hydrOntology-native, and cree) to a single target on-
tology (SWO). The evaluation process is based on three subtasks: given an entity from
the source ontology, identify all related entities in the source and target ontology; given
an entity in the source ontology and the set of related entities, identify the logical re-
lation that holds between them; identify the full complex correspondences. The first
subtask was evaluated based on precision and recall and the latter two were evaluated
using semantic precision and recall.
The GeoLink dataset derives from the homonymous project, funded under the U.S.
National Science Foundation’s EarthCube initiative. It is composed of two ontologies:
the GeoLink Base Ontology (GBO) and the GeoLink Modular Ontology (GMO). The
GeoLink project is a real-world use case of ontologies, and instance data is available.
The alignment between the two ontologies was developed in consultation with domain
experts from several geoscience research institutions. More detailed information on this
benchmark can be found in [48]. Evaluation was done in the same way as with the
Hydrography dataset. The evaluation platform was a MacBook Pro with a 2.6 GHz
Intel Core i5 processor and 16 GB of 1600 MHz DDR3 RAM running macOS Mojave
version 10.14.2.
The Taxon dataset is composed of four knowledge bases containing knowledge
about plant taxonomy: AgronomicTaxon, AGROVOC, TAXREF-LD and DBpedia. The
evaluation is two-fold: first, the precision of the output alignment is manually assessed;
then, a set of source queries are rewritten using the output alignment. The rewritten tar-
get query is then manually classified as correct or incorrect. A source query is consid-
ered successfully rewritten if at least one of the target queries is semantically equivalent
to it. The proportion of source queries successfully rewritten is then calculated (QWR
in the results table). The evaluation over this dataset is open to all matching systems
(simple or complex) but some queries can not be rewritten without complex correspon-
dences. The evaluation was performed with an Ubuntu 16.04 machine configured with
16GB of RAM running under a i7-4790K CPU 4.00GHz x 8 processors.
4 Results and Discussion
4.1 Participation
Following an initial period of growth, the number of OAEI participants has remained
approximately constant since 2012, at slightly over 20. This year we observed a slight
decrease to 19 participating systems. Table 6 lists the participants and the tracks in
which they competed. Some matching systems participated with different variants
(AML, LogMap) whereas others were evaluated with different configurations, as re-
quested by developers (see test case sections for details).
Table 6. Participants and the status of their submissions.
LogMap-Bio
Holontology
ALOD2vec
EVOCROS
POMAP++
FCAMapX
LogMapLt
CANARD
KEPLER
SANOM
Total=19
RADON
LogMap
System
DOME
AMLC
XMap
ALIN
AML
Lily
Silk
Confidence - - X X X X X X - X X X X - X X X X X 14
Anatomy # # # # # 14
Biodiversity & Ecology # # # # # # # # G
# # # # 8
Conference # # # # # # # 12
Disease & Phenotype # # # # # # # G
# # # # 9
Large Biomedical Ont. # G
# # # # # G
# # #
G # # # 10
Multifarm # # # # #
G #
G # # #
G # # # # # # # 6
IIMB # # # # # # # # # # # # # # # # # 2
Link Discovery # # # # # # # # # # # # # # #
G # G
# # 3
SPIMBENCH # # # # # # # # # # # # # # # # 3
Knowledge Graph # # G
# # # # # # # G
# # # # # 7
Interactive Matching # # # # # # # # # # # # # # # 4
Complex Matching # G
# #
G G
# G
# G
# # G
# G
# #
G # G
# G
# G
# G
# # # # #
G 13
total 3 4 12 1 1 7 1 4 4 7 5 11 6 7 6 1 2 1 8 65
Confidence pertains to the confidence scores returned by the system, with X indicating that they
are non-boolean; # indicates that the system did not participate in the track; indicates that it
participated fully in the track; and G
# indicates that it participated in or completed only part of the
tasks of the track.
A number of participating systems use external sources of background knowledge,
which are especially critical in matching ontologies in the biomedical domain. LogMap-
Bio uses BioPortal as mediating ontology provider, that is, it retrieves from BioPortal
the most suitable top-10 ontologies for each matching task. LogMap uses normaliza-
tions and spelling variants from the general (biomedical) purpose SPECIALIST Lexi-
con. 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). XMAP and Lily
use a dictionary of synonyms (pre)extracted from the UMLS Metathesaurus. In addi-
tion Lily also uses a dictionary of synonyms (pre)extracted from BioPortal.
4.2 Anatomy
The results for the Anatomy track are shown in Table 7.
Table 7. Anatomy results, ordered by F-measure. Runtime is measured in seconds; “size” is the
number of correspondences in the generated alignment.
System Runtime Size Precision F-measure Recall Recall+ Coherent
√
AML 42 1493 0.95 0.943 0.936 0.832
√
LogMapBio 808 1550 0.888 0.898 0.908 0.756
POMAP++ 210 1446 0.919 0.897 0.877 0.695 -
√
XMap 37 1413 0.929 0.896 0.865 0.647
√
LogMap 23 1397 0.918 0.88 0.846 0.593
SANOM 487 1450 0.888 0.865 0.844 0.632 -
FCAMapX 118 1274 0.941 0.859 0.791 0.455 -
KEPLER 244 1173 0.958 0.836 0.741 0.316 -
Lily 278 1382 0.872 0.832 0.795 0.518 -
LogMapLite 18 1147 0.962 0.828 0.728 0.288 -
ALOD2Vec 75 987 0.996 0.785 0.648 0.086 -
StringEquiv - 946 0.997 0.766 0.622 0.000 -
DOME 22 935 0.997 0.761 0.615 0.009 -
√
ALIN 271 928 0.998 0.758 0.611 0.0
Holontology 265 456 0.976 0.451 0.294 0.005 -
Of the 14 systems participating in the Anatomy track, 11 achieved an F-measure
higher than the StringEquiv baseline. Three systems were first time participants
(ALOD2Vec, DOME, and Holontology) and showed modest results in terms of both
F-measure and recall+, with only ALOD2Vec ranking above the baseline. Among the
five systems that participated for the second time, SANOM shows increases in both
F-measure (from 0.828 to 0.865) and recall+ (from 0.419 to 0.632), KEPLER and Lily
have the same performance as last year, and both POMAP++ (POMap in 2017) and
FCAMapX (FCA Map in 2016) have decreases in F-measure and recall+. Long-term
systems showed few changes in comparison with previous years with respect to align-
ment quality (precision, recall, F-measure, and recall+), size or run time. The exceptions
were LogMapBio which increased in both recall+ (from 0.733 to 0.756) and alignment
size (by 16 correspondences) since last year, and ALIN that had a substantial increase
of 412 correspondences since last year.
In terms of run time, 6 out of 14 systems computed an alignment in less than 100
seconds, a ratio which is similar to 2017 (5 out of 11). LogMapLite remains the system
with the shortest runtime. Regarding quality, AML remains the system with the high-
est F-measure (0.943) and recall+ (0.832), but 4 other systems obtained an F-measure
above 0.88 (LogMapBio, POMap++, XMap, and LogMap) which is at least as good as
the best systems in OAEI 2007-2010. Like in previous years, there is no significant cor-
relation between the quality of the generated alignment and the run time. Five systems
produced coherent alignments, which is the same as last year.
4.3 Biodiversity and Ecology
Of the 8 participants registered for this track, 7 systems (AML, LogMap, LogMapBio,
LogMapLt, Lily, XMap and POMap) managed to generate a meaningful output in 4
hours, and only KEPLER did not. Table 8 shows the results for the FLOPO-PTO and
ENVO-SWEET tasks.
Table 8. Results for the Biodiversity & Ecology track, ordered by F-measure.
System Size Precision F-measure Recall
FLOPO-PTO task
AML 233 0.88 0.86 0.84
LogMap 235 0.817 0.802 0.787
LogMapBio 239 0.803 0.795 0.787
XMap 153 0.987 0.761 0.619
LogMapLite 151 0.987 0.755 0.611
POMap 261 0.663 0.685 0.709
LiLy 176 0.813 0.681 0.586
ENVO-SWEET task
AML 791 0,776 0,844 0,926
LogMap 583 0,839 0,785 0,738
POMap 583 0,839 0,785 0,738
XMap 547 0,868 0,785 0,716
LogMapBio 572 0,839 0,777 0,724
LogMapLite 740 0,732 0,772 0,817
LiLy 491 0,866 0,737 0,641
Regarding the FLOPO-PTO task, the top 3 ranked systems in terms of F-measure
are AML, LogMap and LogMapBio, with curiously a similar number of generated cor-
respondences among them. Among these, AML achieved the highest F-measure (0.86)
and a well-balanced result, with over 80% recall and a still quite high precision.
Regarding the ENVO-SWEET task, AML ranked first in terms of F-measure, fol-
lowed by a three-way tie between LogMap, POMAP and XMap. AML had a less bal-
anced result in this test case, with a very high recall and significant larger alignment
than the other top systems, but a comparably lower precision. LogMap and POMap
produced alignments of exactly equal size and quality, whereas XMap had the highest
precision overall, but a lower recall than these.
Overall, in this first evaluation, the results obtained from participating systems are
quite promising, as all systems achieved more than 0.68 in term of F-measure. We
should note that most of the participating systems, and all of the most successful ones
use external resources as background knowledge.
4.4 Conference
The conference evaluation results using the sharp reference alignment rar2 are shown
in Table 9. For the sake of brevity, only results with this reference alignment and con-
sidering both classes and properties are shown. For more detailed evaluation results,
please check conference track’s web page.
With regard to the two baselines we can group the twelve participants into four
groups: six matching systems outperformed both baselines (SANOM, AML, LogMap,
XMap, FCAMapX and DOME); three performed the same as the edna baseline (ALIN,
LogMapLt and Holontology); two performed slightly worse than this baseline (KE-
PLER and ALOD2Vec); and Lily performed worse than both baselines. Note that two
systems (ALIN and Lily) do not match properties at all which naturally has a negative
effect on their overall performance.
The performance of all matching systems regarding their precision, recall and F1 -
measure is plotted in Figure 1. Systems are represented as squares or triangles, whereas
the baselines are represented as circles.
With respect to logical coherence [42, 43], only three tools (ALIN, AML and
LogMap) have no consistency principle violation (in comparison to five tools last year
and seven tools two years ago). This year all tools have some conservativity principle
violations (in comparison to one tool having no conservativity principle violation last
year). We should note that these conservativity principle violations can be “false pos-
itives” since the entailment in the aligned ontology can be correct although it was not
derivable in the single input ontologies.
The Conference evaluation results using the uncertain reference alignments are pre-
sented in Table 10.
Among the twelve participating alignment systems, six use 1.0 as the confidence
value for all matches they identify (ALIN, ALOD2Vec, DOME, FCAMapX, Holontol-
ogy, LogMapLt), whereas the remaining six have a wide range of confidence values
(AML, KEPLER, Lily, LogMap, SANOM and XMap).
When comparing the performance of the matchers on the uncertain reference align-
ments versus that on the sharp version (with the corresponding ra1), we see that in
the discrete case all matchers except Lily performed the same or better in terms of F-
measure (Lily’s F-measure dropped by 0.01). The changes in F-measure ranged from
-1 to 15 percent over the sharp reference alignment. This was predominantly driven by
increased recall, which is a result of the presence of fewer ’controversial’ matches in
the uncertain version of the reference alignment.
The performance of the matchers with confidence values always 1.0 is very sim-
ilar regardless of whether a discrete or continuous evaluation methodology is used,
because many of their correspondences are ones that the experts had high agreement
Table 9. 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.
System Prec. F0.5 -m. F1 -m. F2 -m. Rec. Inc.Align. Conser.V. Consist.V.
SANOM 0.72 0.71 0.7 0.69 0.68 9 103 92
AML 0.78 0.74 0.69 0.65 0.62 0 39 0
LogMap 0.77 0.72 0.66 0.6 0.57 0 25 0
XMap 0.76 0.7 0.62 0.56 0.52 4 53 14
FCAMapX 0.64 0.62 0.59 0.56 0.54 11 124 273
DOME 0.74 0.66 0.57 0.5 0.46 3 106 10
edna 0.74 0.66 0.56 0.49 0.45
ALIN 0.82 0.69 0.56 0.48 0.43 0 2 0
Holontology 0.73 0.65 0.56 0.49 0.45 3 66 10
LogMapLt 0.68 0.62 0.56 0.5 0.47 5 96 25
ALOD2Vec 0.67 0.62 0.55 0.5 0.47 6 124 27
KEPLER 0.67 0.61 0.55 0.49 0.46 12 123 159
StringEquiv 0.76 0.65 0.53 0.45 0.41
Lily 0.54 0.53 0.52 0.51 0.5 9 140 124
FCAMapX
AML
KEPLER
LogMap
LogMapLt
F1 -measure=0.7 DOME
Holontology
F1 -measure=0.6 ALOD2Vec
XMap
F1 -measure=0.5
Lily
SANOM
ALIN
edna
StringEquiv
rec=1.0 rec=.8 rec=.6 pre=.6 pre=.8 pre=1.0
Fig. 1. 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].
Table 10. F-measure, precision, and recall of matchers when evaluated using the sharp (ra1),
discrete uncertain and continuous uncertain metrics. Sorted according to F1 -m. in continuous.
Sharp Discrete Continuous
System
Prec. F1 -m. Rec. Prec. F1 -m. Rec. Prec. F1 -m. Rec.
AML 0.84 0.74 0.66 0.79 0.78 0.77 0.80 0.77 0.74
SANOM 0.79 0.74 0.69 0.71 0.74 0.78 0.65 0.72 0.81
ALIN 0.88 0.60 0.46 0.88 0.69 0.57 0.88 0.70 0.59
XMap 0.81 0.65 0.54 0.66 0.74 0.83 0.74 0.70 0.66
DOME 0.79 0.60 0.48 0.79 0.68 0.60 0.78 0.69 0.62
Holontology 0.78 0.59 0.48 0.78 0.68 0.60 0.78 0.68 0.61
ALOD2Vec 0.71 0.59 0.50 0.71 0.66 0.62 0.71 0.67 0.63
LogMap 0.82 0.69 0.59 0.77 0.73 0.70 0.80 0.67 0.57
LogMapLt 0.73 0.59 0.50 0.73 0.67 0.62 0.72 0.67 0.63
FCAMapX 0.68 0.61 0.56 0.65 0.66 0.67 0.64 0.66 0.68
KEPLER 0.76 0.59 0.48 0.76 0.67 0.60 0.58 0.63 0.68
Lily 0.59 0.56 0.53 0.52 0.55 0.59 0.59 0.32 0.22
about, while the ones they missed were more controversial. AML produces a fairly
wide range of confidence values and has the highest F-measure under both the con-
tinuous and discrete evaluation methodologies, indicating that this system’s confidence
evaluation does a good job of reflecting cohesion among experts on this task. Of the re-
maining systems, four (KEPLER, LogMap, SANOM and XMap) have relatively small
drops in F-measure when moving from discrete to continuous evaluation. Lily’s perfor-
mance drops drastically under the continuous evaluation methodology. This is because
the matcher assigns low confidence values to some correspondences in which the labels
are equivalent strings, which many experts agreed with unless there was a compelling
reason not to. This hurts recall, but using a low threshold value in the discrete version
of the evaluation metrics ’hides’ this problem.
Overall, in comparison with last year, the F-measures of most returning matching
systems essentially held constant under both the sharp and uncertain evaluations. The
exceptions were ALIN and SANOM, whose performance improved substantially. In
fact, the latter improved its performance so much that it became the top system with re-
gard to F-measure according to the sharp evaluation. We can conclude that all matchers
perform better on the fuzzy versus sharp version of the benchmark and that the perfor-
mance of AML against the fuzzy reference alignment rivals that of a human evaluated
in the same way.
4.5 Disease and Phenotype Track
In the OAEI 2018 phenotype track 9 systems were able to complete at least one of the
tasks with a 6 hours timeout. Tables 11 show the evaluation results in the HP-MP and
DOID-ORDO matching tasks, respectively.
Since the consensus reference alignments only allow us to assess how systems per-
form in comparison with one another, the proposed ranking is only a reference. Note
that some of the correspondences in the consensus alignment may be erroneous (false
Table 11. Results for the HP-MP and DOID-ORDO tasks based on the consensus reference
alignment.
Scores Incoherence
System Time (s) # Corresp. # Unique
Prec. F-m. Rec. Unsat. Degree
HP-MP task
LogMap 31 2,130 1 0.88 0.86 0.84 0 0.0%
LogMapBio 821 2,178 37 0.86 0.85 0.84 0 0.0%
AML 70 2,010 279 0.89 0.84 0.80 0 0.0%
LogMapLt 7 1,370 3 0.99 0.76 0.61 0 0.0%
POMAP++ 1,668 1,502 214 0.86 0.69 0.58 0 0.0%
Lily 4,749 2,118 733 0.68 0.66 0.65 0 0.0%
XMap 20 704 2 0.99 0.48 0.31 0 0.0%
DOME 46 689 0 1.00 0.47 0.31 0 0.0%
DOID-ORDO task
LogMap 25 2,323 0 0.94 0.85 0.78 0 0.0%
LogMapBio 1,891 2,499 91 0.90 0.85 0.80 0 0.0%
POMAP++ 2,264 2,563 174 0.87 0.83 0.80 0 0.0%
LogMapLt 7 1,747 16 0.99 0.76 0.62 0 0.0%
XMap 15 1,587 37 0.97 0.70 0.55 0 0.0%
KEPLER 2,746 1,824 158 0.88 0.70 0.57 0 0.0%
Lily 2,847 3,738 1,167 0.59 0.67 0.78 206 1.9%
AML 135 4,749 1,886 0.51 0.65 0.87 0 0.0%
DOME 10 1,232 2 1.00 0.61 0.44 0 0.0%
positives) because all systems that agreed on it could be wrong (e.g., in erroneous corre-
spondences with equivalent labels, which are not that uncommon in biomedical tasks).
In addition, the consensus alignments will not be complete, because there are likely to
be correct correspondences that no system is able to find, and there are a number of
correspondences found by only one system (and therefore not in the consensus align-
ments) which may be correct. Nevertheless, the results with respect to the consensus
alignments do provide some insights into the performance of the systems.
Overall, LogMap is the system that provides the closest set of correspondences
to the consensus (not necessarily the best system) in both tasks. It has a small set
of unique correspondences as most of its correspondences are also suggested by its
variant LogMapBio and vice versa. By contrast, Lily and AML produce the highest
number of unique correspondences in HP-MP and DOID-ORDO respectively, and the
second-highest inversely. All systems produce coherent alignments except for Lily in
the DOID-ORDO task.
4.6 Large Biomedical Ontologies
In the OAEI 2018 Large Biomedical Ontologies track, 10 systems were able to com-
plete at least one of the tasks within a 6 hours timeout. Seven systems were able to
complete all six tasks.17 Since the reference alignments for this track are based on the
17
Check out the supporting scripts to reproduce the evaluation: https://github.com/
ernestojimenezruiz/oaei-evaluation
Table 12. Results for the whole ontologies matching tasks in the OAEI largebio track.
Scores Incoherence
System Time (s) # Corresp. # Unique
Prec. F-m. Rec. Unsat. Degree
Whole FMA and NCI ontologies (Task 2)
AML 55 2,968 311 0.84 0.86 0.87 2 0.014%
LogMap 1,072 2,701 0 0.86 0.83 0.81 2 0.014%
LogMapBio 1,072 2,860 39 0.83 0.83 0.83 2 0.014%
XMap2 65 2,415 52 0.88 0.80 0.74 2 0.014%
FCAMapX 881 3,607 443 0.67 0.74 0.84 8,902 61.8%
LogMapLt 6 3,458 250 0.68 0.74 0.82 5,170 35.9%
DOME 12 2,383 10 0.80 0.73 0.67 596 4.1%
Whole FMA ontology with SNOMED large fragment (Task 4)
FCAMapX 1,736 7,971 1,258 0.82 0.79 0.76 21,289 57.0%
AML 94 6,571 462 0.88 0.77 0.69 0 0.0%
LogMapBio 1,840 6,471 31 0.83 0.73 0.65 0 0.0%
LogMap 288 6,393 0 0.84 0.73 0.65 0 0.0%
XMap2 299 6,749 1,217 0.72 0.66 0.61 0 0.0%
LogMapLt 9 1,820 56 0.85 0.33 0.21 981 2.6%
DOME 20 1,588 1 0.94 0.33 0.20 951 2.5%
Whole NCI ontology with SNOMED large fragment (Task 6)
AML 168 13,176 1,230 0.90 0.77 0.67 ≥517 ≥0.6%
FCAMapX 2,377 15,383 1,670 0.80 0.73 0.68 ≥72,859 ≥85.5%
LogMapBio 2,942 13,098 231 0.85 0.72 0.63 ≥3 ≥0.004%
LogMap 475 12,276 0 0.87 0.71 0.60 ≥1 ≥0.001%
LogMapLt 11 12,864 720 0.80 0.66 0.57 ≥74,013 ≥86.9%
DOME 24 9,702 42 0.91 0.63 0.49 ≥53,574 ≥62.9%
XMap2 427 16,271 4,432 0.64 0.61 0.58 ≥73,571 ≥86.4%
UMLS-Metathesaurus, we disallowed the use of this resource as a source of background
knowledge in the matching systems that used it, XMap and Lily. XMap was still able
to produce competitive results, while Lily produced an empty set of alignments. The
evaluation results for the largest matching tasks are shown in Tables 12.
The top-ranked systems by F-measure were respectively: AML and LogMap in Task
2; FCAMapX and AML in Task 4; and AML and FCAMapX in Task 6.
Interestingly, the use of background knowledge led to an improvement in recall from
LogMap-Bio over LogMap in all tasks, but this came at the cost of precision, resulting
in the two variants of the system having very similar F-measures.
The effectiveness of all systems decreased from small fragments to whole ontolo-
gies tasks.18 One reason for this is that with larger ontologies there are more plausible
correspondence candidates, and thus it is harder to attain both a high precision and a
high recall. In fact, this same pattern is observed moving from the FMA-NCI to the
FMA-SNOMED to the SNOMED-NCI problem, as the size of the task also increases.
Another reason is that the very scale of the problem constrains the matching strategies
18
http://www.cs.ox.ac.uk/isg/projects/SEALS/oaei/2018/results/
that systems can employ: AML for example, forgoes its matching algorithms that are
computationally more complex when handling very large ontologies, due to efficiency
concerns.
The size of the whole ontologies tasks proved a problem for a number of systems,
which were unable to complete them within the allotted time: POMAP++, ALOD2Vec
and KEPLER.
With respect to alignment coherence, as in previous OAEI editions, only three dis-
tinct systems have shown alignment repair facilities: AML, LogMap and its LogMap-
Bio variant, and XMap (which reuses the repair techniques from Alcomo [34]). Note
that only LogMap and LogMap-Bio are able to reduce to a minimum the number of
unsatisfiable classes across all tasks, missing 9 unsatisfiable classes in the worst case
(whole FMA-NCI task). XMap seems to deactivate the repair facility for the SNOMED-
NCI case.
As the results tables show, even the most precise alignment sets may lead to a huge
number of unsatisfiable classes. This proves the importance of using techniques to as-
sess 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 [34], the repair
module of LogMap (LogMap-Repair) [28] or the repair module of AML [39], which
have worked well in practice [30, 21].
4.7 Multifarm
This year, 6 matching systems registered for the MultiFarm track: AML, DOME,
EVOCROS, KEPLER, LogMap and XMap. This represents a slight decrease from the
last two years, but is within an approximately constant trend (8 in 2017, 7 in 2016, 5 in
2015, 3 in 2014, 7 in 2013, and 7 in 2012). However, a few systems had issues when
evaluated: i) KEPLER generated some parsing errors for some pairs; ii) EVOCROS
took around 30 minutes to complete a single task (we have hence tested only 50 match-
ing tasks) and generated empty alignments; iii) DOME was not able to generate any
alignment; iv) XMap had problems dealing with most pairs involving the ar, ru and cn
languages. Please refer to the OAEI papers of the matching systems for a detailed de-
scription of the strategies employed by each system, most of which adopt a translation
step before the matching itself.
The Multifarm evaluation results based on the blind dataset are presented in Ta-
ble 13. They have been computed using the Alignment API 4.9 and can slightly dif-
fer from those computed with the SEALS client. We do not report the results of non-
specific systems here, as we could observe in the last campaigns that they can have
intermediate results in the “same ontologies” task (ii) and poor performance in the “dif-
ferent ontologies” task (i).
With respect to run time, we observe large differences between systems due to the
high number of matching tasks involved (55 x 24). Note as well that the concurrent
access to the SEALS repositories during the evaluation period may have an impact on
the time required for completing the tasks.
Table 13. MultiFarm aggregated results per matcher, for each type of matching task – different
ontologies (i) and same ontologies (ii).
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 26 55 6.87 .72 (.72) .46 (.46) .35 (.35) 23.24 .96 (.95) .27 (.27) .16 (.16)
KEPLER 900 53 9.74 .40 (.42) .27 (.28) .21 (.22) 58.28 .85 (.88) .49 (.51) .36 (.37)
LogMap 39 55 6.99 .72 (.72) .37 (.37) .25 (.25) 46.80 .95 (.96) .41 (.42) .28 (.28)
XMap 22 26 94.72 .02 (.05) .03 (.07) .07 (.07) 345.00 .13 (.18) .14 (.20) .19 (.19)
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 generate (non-empty) alignments; size
indicates the average of the number of generated correspondences for the tests where an (non-
empty) alignment has been generated. Two kinds of results are reported: those 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.
In terms of F-measure, AML remains the top performing system in task (i), followed
by LogMap and KEPLER. In task (ii), AML has relatively low performance (with a
notably low recall) and KEPLER has the highest F-measure, followed by LogMap.
With respect to the pairs of languages for test cases of type (i), for the sake of brevity,
we do not present the detailed results. Please refer to the OAEI results web page to
view them. The language pairs in which systems perform better in terms of F-measure
include: es-it, it-pt and nl-pt (AML); cz-pt and de-pt (KEPLER); en-nl (LogMap); and
cz-en (XMap). We note also some patterns behind the worst results obtained by systems:
ar-cn for AML, and some pairs involving cn for KEPLER and LogMap)
In terms of performance, the F-measure for blind tests remains relatively stable
across campaigns. AML and LogMap keep their positions and have similar F-measure
with respect to the previous campaigns, as does XMap. As observed in previous cam-
paigns, systems privilege precision over recall, and the results are expectedly below
the ones obtained for the original Conference dataset. Cross-lingual approaches re-
main mainly based on translation strategies and the combination of other resources
(like cross-lingual links in Wikipedia, BabelNet, etc.) while strategies such as machine
learning, or indirect alignment composition remain under-exploited.
4.8 IIMB
Only two systems participated in the new IIMB track: AML and LogMap. The obtained
results are summarized in Table 1419 .
In the results of both AML and LogMap, we note that high-quality performances
are provided on test-cases based on DVL transformations. We note that the evaluation
results on this kind of matching issues have been improved in the recent years (for in-
stance, see [3] for a comparison against the 2012 version of the IIMB dataset). As a
matter of fact, recognition of similarities across instance descriptions with data-value
19
A detailed report of test-case results is provided on https://islab.di.unimi.it/
im_oaei_2018/.
Table 14. Summary of the IIMB results.
System Runtime (s) Precision Recall F-measure
Data Value Transformations
AML 1828 0.893 0.789 0.828
LogMap 4.2 0.896 0.893 0.889
Data Structure Transformations
AML 2036 0.419 0.433 0.424
LogMap 5.7 0.934 0.985 0.959
Data Semantics Transformations
AML 6.2 0.747 0.889 0.796
LogMap 4.6 0.855 0.947 0.893
Mixed Transformations
AML 2083 0.334 0.294 0.295
LogMap 6.5 0.920 0.758 0.819
heterogeneities represents a sort of consolidated matching capability that can be con-
sidered as a standard functionality of the current state-of-the-art tools. We also note that
promising results are also provided by both the participating tools on test-cases based
on DSS transformations. We argue that such a kind of result is due to the capability of
both AML and LogMap to cope with incoherence, thus reducing the number of false-
positive results. As a final remark, we observe that recall is usually lower than precision.
Maybe, the cause is the non-uniform quality of expected automatically-generated cor-
respondences. Expected correspondences are created by applying a sequence of trans-
formations with different length (i.e., number of transformations) and different degree
of complexity (i.e., strength of applied data manipulations). Sometimes, the applied
SWING transformations produce correspondences that are more difficult to agree with,
rather than to detect. Measuring the quality of automatically-generated alignments as
well as pruning of excessively-hard ones from the set of expected results is a challeng-
ing issue to consider in future research work (see Section 5).
4.9 Link Discovery
This year the Link Discovery track counted one participant in the Linking test case
(AML) and three participants in the Spatial test case: AML, Silk and RADON.
In the Linking test case, AML perfectly captures all the correct links while not
producing wrong ones, thus obtaining perfect precision and a recall (1.0) in both the
Sandbox and Mainbox datasets. It required 6.8s and 313s, respectively, to complete the
two tasks.
We divided the Spatial test cases into four suites. In the first two suites (SLL and
LLL), the systems were asked to match LineStrings to LineStrings considering a given
relation for 200 and 2K instances for the TomTom and Spaten datasets. In the last two
tasks (SLP, LLP), the systems were asked to match LineStrings to Polygons (or Poly-
gons to LineStrings depending on the relation) again for both datasets. Since the pre-
cision, recall and f-measure results from all systems were equal to 1.0, we are only
presenting results regarding the time performance. The time performance of the match-
ing systems in the SLL, LLL, SLP and LLP suites are shown in Figures 2-3.
In the SLL suite, RADON has the best performance in most cases except for the
Touches and Intersects relations, followed by AML. Silk seems to need the most time,
particularly for Touches and Intersects relations in the TomTom dataset and Overlaps
in both datasets.
In the LLL suite we have a more clear view of the capabilities of the systems with
the increase in the number of instances. In this case, RADON and Silk have similar
behavior as in the the small dataset, but it is more clear that the systems need much
more time to match instances from the TomTom dataset. RADON has still the best
performance in most cases. AML has the next best performance and is able to handle
some cases better than other systems (e.g. Touches and Intersects), however, it also hits
the platform time limit in the case of Disjoint.
In the SLP suite, in contrast to the first two suites, RADON has the best performance
for all relations. AML and Silk have minor time differences and, depending on the case,
one is slightly better than the other. All the systems need more time for the TomTom
dataset but due to the small size of the instances the time difference is minor.
In the LLP suite, RADON again has the best performance in all cases. AML hits the
platform time limit in Disjoint relations on both datasets and is better than Silk in most
cases except Contains and Within on the TomTom dataset where it needs an excessive
amount of time.
Taking into account the executed test cases we can identify the capabilities of the
tested systems as well as suggest some improvements. All the systems participated in
most of the test cases, with the exception of Silk which did not participate in the Covers
and Covered By test cases.
RADON was the only system that successfully addressed all the tasks, and had the
best performance for the SLP and LLP suites, but it can be improved for the Touches
and Intersects relations for the SLL and LLL suites. AML performs extremely well in
most cases, but can be improved in the cases of Covers/Covered By and Contains/Within
when it comes to LineStrings/Polygons Tasks and especially in Disjoint relations where
it hits the platform time limit. Silk can be improved for the Touches, Intersects and
Overlaps relations and for the SLL and LLL tasks and for the Disjoint relation in SLP
and LLP Tasks.
In general, all systems needed more time to match the TomTom dataset than the
Spaten one, due to the smaller number of points per instance in the latter. Comparing the
LineString/LineString to the LineString/Polygon Tasks we can say that all the systems
needed less time for the first for the Contains, Within, Covers and Covered by relations,
more time for the Touches, Instersects and Crosses relations, and approximately the
same time for the Disjoint relation.
4.10 SPIMBENCH
This year, the SPIMBENCH track counted three participants: AML, Lily, and LogMap.
The evaluation results of the track are shown in Table 15.
Lily had the best performance overall both in terms of F-measure and in terms
of run time. Notably, its run time scaled very well with the increase in the number of
Fig. 2. Time performance for TomTom & Spaten SLL (top) and LLL (bottom) suites for AML
(A), Silk (S) and RADON (R).
instances. Both Lily and AML had a higher recall than precision, with the former having
full recall. By contrast, LogMap had the highest precision but lowest recall of the three
systems. AML and LogMap had a similar run time for the Sandbox task, but the latter
scaled better with the increase in the number of instances.
Fig. 3. Time performance for TomTom & Spaten SLP (top) and LLP (bottom) suites for AML
(A), Silk (S) and RADON (R).
Table 15. SPIMBENCH track results.
System Precision Recall F-measure Time (ms)
Sandbox (100 instances)
AML 0.835 0.896 0.865 6220
Lily 0.849 1.0 0.919 1960
LogMap 0.938 0.763 0.841 5887
Mainbox (5000 instances)
AML 0.839 0.884 0.860 37190
Lily 0.855 1.0 0.922 3103
LogMap 0.893 0.709 0.791 23494
4.11 Knowledge Graph
We evaluated all SEALS participants in the OAEI (even those not registered for the
track) on a very small matching example20 . This revealed that not all systems were able
to cope with the task, and in the end only the following systems were evaluated: AML,
POMap++, Hontology, DOME, LogMap (in its KG version), LogMapBio, LogMapLt.
Of these systems, the following were able output results for all nine test cases:
POMAP++, Holontology, DOME, LogMapBio and the baseline. AML ran out of time
(12 hours) on some tracks, LogMap needed more than the given 32 GB RAM for the
bigger knowledge graphs, and LogMapLt created alignment files bigger than 1GB (up
to 50 GB in some runs).
Table 16 shows the aggregated results for each system, including the number of
tasks in which it was able to generate a non-empty alignment (#tasks) and the av-
erage number of generated correspondences in those tasks (size). In addition to the
global average precision, F-measure, and recall results, in which tasks where systems
produced empty alignments were counted, we also computed F-measure and recall ig-
noring empty alignments (note that precision is the same) which are shown between
parentheses in the table, where applicable.
All systems were able to generate class correspondences, but only the three tasks
from the Games topic have enough classes to be meaningfully matched. The base-
line has an F-Measure of 0.79 which is surpassed by AML, Holontology, LogMap and
LogMapBio (when considering only completed tracks).
DOME was the only system able to produce property correspondences (in addition
to the baseline). The remaining systems do not return any property correspondences,
probably because all properties are typed as rdf:Property and not subdivided into
owl:DatatypeProperty and owl:ObjectProperty. However, this cannot be
done easily in a preprocessing step because the usage of the properties is not strict, i.e.,
some properties are used both with literals and resources as their object. Given that a
system that matches only OWL properties of the same type would not be able to handle
such cases as this, an improvement of these matching systems would be to include also
the ability of correspondence rdf:Property in case no more types are defined.
20
http://oaei.ontologymatching.org/2018/results/knowledgegraph/
small_test.zip
Table 16. Knowledge Graph track results, divided into class, property, instance, and overall cor-
respondences.
System Time (s) # tasks Size Prec. F-m. Rec.
Class performance
AML 88448 5 11.6 0.85 0.64 (0.87) 0.51 (0.88)
POMAP++ 438 9 15.1 0.79 0.74 0.69
Holontology 318 9 16.8 0.80 0.83 0.87
DOME 13747 9 16.0 0.73 0.73 0.73
LogMap 14083 7 21.7 0.66 0.77 (0.80) 0.91 (1.00)
LogMapBio 2340 9 22.1 0.68 0.81 1.00
LogMapLt 500 6 22.0 0.61 0.72 (0.76) 0.87 (1.00)
Baseline 412 9 18.9 0.75 0.79 0.84
Property performance
AML 88448 5 0.0 0.00 0.00 0.00
POMAP++ 438 9 0.0 0.00 0.00 0.00
Holontology 318 9 0.0 0.00 0.00 0.00
DOME 13747 9 207.3 0.86 0.84 0.81
LogMap 14083 7 0.0 0.00 0.00 0.00
LogMapBio 2340 9 0.0 0.00 0.00 0.00
LogMapLt 500 6 0.0 0.00 0.00 0.00
Baseline 412 9 213.8 0.86 0.84 0.82
Instance performance
AML 88448 5 82380.9 0.16 0.23 (0.26) 0.38 (0.63)
POMAP++ 438 9 0.0 0.00 0.00 0.00
Holontology 318 9 0.0 0.00 0.00 0.00
DOME 13747 9 15688.7 0.61 0.61 0.61
LogMap 14083 7 97081.4 0.08 0.14 (0.15) 0.81 (0.93)
LogMapBio 2340 9 0.0 0.00 0.00 0.00
LogMapLt 500 6 82388.3 0.39 0.52 (0.56) 0.76 (0.96)
Baseline 412 9 17743.3 0.59 0.69 0.82
Overall performance
AML 88448 5 102471.1 0.19 0.23 (0.28) 0.31 (0.52)
POMAP++ 438 9 16.9 0.79 0.14 0.08
Holontology 318 9 18.8 0.80 0.17 0.10
DOME 13747 9 15912.0 0.68 0.68 0.67
LogMap 14083 7 97104.8 0.09 0.16 (0.16) 0.64 (0.74)
LogMapBio 2340 9 24.1 0.68 0.19 0.11
LogMapLt 500 6 88893.1 0.42 0.49 (0.54) 0.60 (0.77)
Baseline 412 9 17976.0 0.65 0.73 0.82
With respect to instance correspondences, AML, DOME, LogMap, LogMapLt were
able to produce them (as was the baseline) whereas POMAP++, Holontology and
LogMapBio were not, since they are not designed for instance matching. The base-
line was unsurpassed by any system in this category in either F-measure or recall. One
reason for this is that the baseline had the highest F-measure among systems able to
match both classes and instances, and had a higher F-measure than DOME at matching
properties, given that the alignment of instances is conditioned by the correct alignment
of classes and properties. Furthermore, many of the matching systems return n:m corre-
spondences and thus a lot of false positive correspondences, resulting in low precision.
We analyzed the errors for a specific task, namely
darkscape-oldschoolrunescape. For this task, the baseline could not
find the following correspondences: Lumbridge and Draynor Tasks =
Lumbridge & Draynor Diary and Cupric sulphate = Cupric sulfate.
The matcher AML does not find Translated notes = Translated notes,
even if the label (wiki page name) is exactly the same. False positive correspondences
for the LogMap matcher are Ancient Magicks = Carrallangar Teleport
and Ancient Magicks = Kharyrll Teleport. For AML one example is
Customs Officer = Gang boss.
Regarding runtime, AML was the slowest system, followed by DOME and LogMap.
POMAP++ and Holontology were quite fast, but only return class correspondences.
4.12 Interactive matching
This year, the same four systems as last year participated in the Interactive matching
track: ALIN, AML, LogMap, and XMap. Their results are shown in Table 17 and Figure
4 for both Anatomy and Conference datasets.
The table includes the following information (column names within parentheses):
– The performance of the system: Precision (Prec.), Recall (Rec.) and F-measure (F-
m.) with respect to the fixed reference alignment, as well as Recall+ (Rec.+) for the
Anatomy task. To facilitate the assessment of the impact of user interactions, we
also provide the performance results from the original tracks, without interaction
(line with Error NI).
– To ascertain the impact of the oracle errors, we provide the performance of the
system with respect to the oracle (i.e., the reference alignment as modified by the
errors introduced by the oracle: Precision oracle (Prec. oracle), Recall oracle (Rec.
oracle) and F-measure oracle (F-m. oracle). For a perfect oracle these values match
the actual performance of the system.
– Total requests (Tot Reqs.) represents the number of distinct user interactions with
the tool, where each interaction can contain one to three conflicting correspon-
dences, that could be analysed simultaneously by a user.
– Distinct correspondences (Dist. Mapps) counts the total number of correspondences
for which the oracle gave feedback to the user (regardless of whether they were
submitted simultaneously, or separately).
– Finally, the performance of the oracle itself with respect to the errors it introduced
can be gauged through the positive precision (Pos. Prec.) and negative precision
(Neg. Prec.), which measure respectively the fraction of positive and negative an-
swers given by the oracle that are correct. For a perfect oracle these values are equal
to 1 (or 0, if no questions were asked).
The figure shows the time intervals between the questions to the user/oracle for the
different systems and error rates. Different runs are depicted with different colors.
Table 17. Interactive matching results for the Anatomy and Conference datasets.
Prec. Rec. F-m. Tot. Dist. Pos. Neg.
Tool Error Prec. Rec. F-m. Rec.+ oracle oracle oracle Reqs. Mapps Prec. Prec.
Anatomy Dataset
NI 0.998 0.611 0.758 0.0 – – – – – – –
0.0 0.994 0.826 0.902 0.543 0.994 0.826 0.902 602 1448 1.0 1.0
ALIN 0.1 0.914 0.802 0.854 0.482 0.994 0.833 0.906 578 1373 0.731 0.965
0.2 0.848 0.784 0.815 0.436 0.994 0.839 0.91 564 1343 0.561 0.931
0.3 0.784 0.757 0.77 0.369 0.995 0.843 0.912 552 1307 0.419 0.875
NI 0.95 0.936 0.943 0.832 – – – – – – –
0.0 0.964 0.948 0.956 0.862 0.964 0.948 0.956 240 240 1.0 1.0
AML 0.1 0.952 0.946 0.948 0.857 0.965 0.95 0.957 268 268 0.719 0.97
0.2 0.938 0.941 0.939 0.849 0.965 0.95 0.957 272 272 0.52 0.935
0.3 0.92 0.938 0.929 0.843 0.966 0.951 0.958 299 299 0.379 0.905
NI 0.918 0.846 0.88 0.593 – – – – – – –
0.0 0.982 0.846 0.909 0.595 0.982 0.846 0.909 388 1164 1.0 1.0
LogMap 0.1 0.961 0.832 0.892 0.568 0.964 0.801 0.875 388 1164 0.742 0.966
0.2 0.945 0.823 0.88 0.552 0.944 0.761 0.842 388 1164 0.567 0.927
0.3 0.932 0.819 0.872 0.543 0.922 0.725 0.812 388 1164 0.434 0.878
NI 0.929 0.865 0.896 0.647 – – – – – – –
0.0 0.929 0.867 0.897 0.653 0.929 0.867 0.897 35 35 1.0 1.0
XMap 0.1 0.929 0.867 0.897 0.653 0.929 0.866 0.896 35 35 0.601 0.978
0.2 0.929 0.867 0.897 0.653 0.929 0.865 0.896 35 35 0.4 0.965
0.3 0.929 0.867 0.897 0.653 0.929 0.863 0.895 35 35 0.298 0.946
Conference Dataset
NI 0.88 0.456 0.601 – – – – – – – –
0.0 0.921 0.721 0.809 – 0.921 0.721 0.809 276 698 1.0 1.0
ALIN 0.1 0.725 0.686 0.705 – 0.934 0.753 0.834 264 674 0.538 0.987
0.2 0.601 0.648 0.623 – 0.942 0.773 0.849 260 657 0.341 0.967
0.3 0.495 0.624 0.552 – 0.951 0.796 0.866 259 645 0.226 0.95
NI 0.841 0.659 0.739 – – – – – – –
0.0 0.912 0.711 0.799 – 0.912 0.711 0.799 270 270 1.0 1.0
AML 0.1 0.838 0.698 0.762 – 0.923 0.733 0.817 277 277 0.691 0.971
0.2 0.769 0.676 0.719 – 0.928 0.747 0.827 271 271 0.533 0.922
0.3 0.715 0.663 0.688 – 0.931 0.758 0.836 270 270 0.459 0.885
NI 0.818 0.59 0.686 – – – – – – – –
0.0 0.886 0.61 0.723 – 0.886 0.61 0.723 82 246 1.0 1.0
LogMap 0.1 0.85 0.596 0.7 – 0.858 0.576 0.69 82 246 0.71 0.978
0.2 0.82 0.588 0.685 – 0.831 0.547 0.66 82 246 0.507 0.941
0.3 0.793 0.583 0.672 – 0.808 0.518 0.631 82 246 0.366 0.907
NI 0.716 0.62 0.665 – – – – – – – –
0.0 0.719 0.62 0.666 – 0.719 0.62 0.666 16 16 0.0 1.0
XMap 0.1 0.719 0.62 0.666 – 0.719 0.617 0.665 16 16 0.0 1.0
0.2 0.718 0.62 0.666 – 0.72 0.613 0.662 16 16 0.2 1.0
0.3 0.718 0.62 0.666 – 0.721 0.613 0.662 16 16 0.1 1.0
NI stands for non-interactive, and refers to the results obtained by the matching system in the
original track.
Fig. 4. Time intervals between requests to the user/oracle for the Anatomy (top 4 plots) and Con-
ference (bottom 4 plots) datasets. 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.
The matching systems that participated in this track employ different user-
interaction strategies. While LogMap, XMap and AML make use of user interactions
exclusively in the post-matching steps to filter their candidate correspondences, ALIN
can also add new candidate correspondences to its initial set. LogMap and AML both
request feedback on only selected correspondences candidates (based on their similar-
ity patterns or their involvement in unsatisfiabilities) and AML presents one correspon-
dence at a time to the user. XMap also presents one correspondence at a time and asks
mainly about incorrect correspondences. ALIN and LogMap can both ask the oracle to
analyze several conflicting correspondences simultaneously.
The performance of the systems usually improves when interacting with a perfect
oracle in comparison with no interaction. The one exception is XMap, because it is
barely interactive in the datasets. In general, XMap performs very few requests to the
oracle compared to the other systems. Thus, it is also the system that improves the
least with user interaction. On the other end of the spectrum, ALIN is the system that
improves the most, because its high number of oracle requests and its non-interactive
performance was the lowest of the interactive systems, and thus the easiest to improve.
Although system performance deteriorates when the error rate increases, 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. Naturally, the more a system relies
on the oracle, the more its performance tends to be affected by the oracle’s errors.
The impact of the oracle’s errors is linear for ALIN, AML and for XMap in most
tasks, as the F-measure according to the oracle remains approximately constant across
all error rates. It is supra-linear for LogMap in all datasets.
Another aspect that was assessed, was the response time of systems, i.e., the time
between requests. Two models for system response times are frequently used in the lit-
erature [9]: Shneiderman and Seow take different approaches to categorize the response
times taking a task-centered view and a user-centered view respectively. According to
task complexity, Shneiderman defines response time in four categories: typing, mouse
movement (50-150 ms), simple frequent tasks (1 s), common tasks (2-4 s) and complex
tasks (8-12 s). While Seow’s definition of response time is based on 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 re-
sponse times (request intervals as we call them above) observed in all datasets fall into
the tolerable and acceptable response times, and even into the first categories, in both
models. The request intervals for AML, LogMap and XMAP stay at a few milliseconds
for most datasets. ALIN’s request intervals are higher, but still in the tenth of second
range. It could be the case, however, that a user would not be able to take advantage
of these low response times because the task complexity may result in higher user re-
sponse time (i.e., the time the user needs to respond to the system after the system is
ready).
4.13 Complex Matching
The only systems able to generate any kind of complex correspondence in any of the
complex matching test cases were AMLC (in the Conference test suite) and CANARD
(in the Taxon test case). No systems were capable of generating complex correspon-
dences over either the Hydrography or the GeoLink test cases.
On the Conference test suite, only complex correspondences were being evaluated,
since simple correspondences are already evaluated under the Conference track. In the
case of the Hydrography and GeoLink test cases, all SEALS OAEI participants were
evaluated in subtask 1 of both test cases, wherein they had to simply identify related
entities. On the Taxon test case, all 14 systems which registered to the complex, confer-
ence and/or anatomy track were evaluated, but only 7 could output at least one align-
ment.
The results of the systems on the four test cases are summarized in Table 18.
Table 18. Results of the Complex Track. The precision, recall and F-measure are the average
measures. QWR is the proportion of queries well rewritten.
Conference Hydrography (subtask 1) GeoLink (subtask 1) Taxon
Matcher
Prec. F-meas. Rec. Prec. F-meas. Rec. Prec. F-meas. Rec. Prec. QWR
ABC - - - 0.43 0.18 0.12 - - - - -
ALOD2Vec - - - 0.5 0.09 0.05 0.78 0.19 0.11 - -
AMLC 0.54 0.42 0.34 - - - - - - - -
AML - - - - - - - - - 0.00 0.00
CANARD - - - - - - - - - 0.20 0.13
DOME - - - 0.35 0.09 0.06 0.44 0.17 0.11 - -
FMapX - - - 0.46 0.11 0.07 - - - - -
Holontology - - - - - - - - - 0.22 0.00
KEPLER - - - 0.5 0.09 0.05 - - - - -
LogMap - - - 0.44 0.08 0.05 0.85 0.18 0.1 0.54 0.07
LogMapBio - - - - - - - - - 0.28 0.00
LogMapKG - - - - - - 0.85 0.18 0.1 - -
LogMapLt - - - - - - 0.73 0.19 0.11 0.16 0.10
POMAP++ - - - 0.42 0.06 0.04 0.9 0.17 0.09 0.14 0.00
XMap - - - 0.21 0.09 0.06 0.39 0.15 0.09 - -
With respect to subtask 1 of the Hydrography and GeoLink test cases, the results
show that a simple baseline approach that identifies target entity names within source
entity comments performs better than most existing matchers. This is unsurprising, as
matching systems are configured to find equivalent concepts rather than related ones.
The takeaway from this year is that there is a lot of room for new approaches on this
task.
In the Taxon test cases, only the output of LogMap, LogMapLt and CANARD could
be used to rewrite source queries.
A more detailed discussion of the results of each task can be found in the OAEI page
for this track. For a first edition of complex matching in an OAEI campaign, and given
the inherent difficulty of the task, the results and participation are promising albeit still
modest.
5 Conclusions & Lessons Learned
The OAEI 2018 counted this year several new tracks, some of which open new per-
spectives in the field, in particular with respect to the generation of more expressive
alignments. We witnessed a slight decrease in the number of participants in comparison
with previous years, but with a healthy mix of new and returning systems. However,
like last year, the distribution of participants by tracks was uneven.
The schema matching tracks saw abundant participation, but, as has been the trend
of the recent years, little substantial progress in terms of quality of the results or run
time of top matching systems, judging from the long-standing tracks. On the one hand,
this may be a sign of a performance plateau being reached by existing strategies and
algorithms, which would suggest that new technology is needed to obtain significant
improvements. On the other hand, it is also true that established matching systems tend
to focus more on new tracks and datasets than on improving their performance in long-
standing tracks, whereas new systems typically struggle to compete with established
ones.
The number of matching systems capable of handling very large ontologies has in-
creased slightly over the last years, but is still relatively modest, judging from the Large
Biomedical Ontologies track. We will aim at facilitating participation in future editions
of this track by providing techniques to divide the matching tasks in manageable sub-
tasks (e.g., [27]).
There has also been progress, but likewise room for improvement, on the ability
of matching systems to match properties, judging from the Conference track. To assist
system developers in tackling this aspect, we plan to provide a more detailed evaluation
in the future, including an analysis of the false positives per matching system.
Less encouraging is the low number of systems concerned with the logical coher-
ence of the alignments they produce, an aspect which is critical for several semantic
web applications. Perhaps a more direct approach is needed to promote this topic, such
as providing a more in-depth analysis of the causes of incoherence in the evaluation or
even organizing a future track focusing on logical coherence alone.
The consensus-based evaluation in the Disease and Phenotype track offers limited
insights into performance, as several matching systems produce a number of unique
correspondences which may or may not be correct. In the absence of a true reference
alignment, future evaluation should seek to determine whether the unique correspon-
dences contain indicators of correctness, such as semantic similarity, or appear to be
noise.
The instance matching tracks and the new instance and schema matching track
counted few participants, as has been the trend in recent years. Part of the reason for
this is that several of these tracks ran on the HOBBIT platform, and the transition
from SEALS to HOBBIT has not been as easy as we might desire. Thus, participation
should increase next year as systems become more familiar with the HOBBIT platform
and have more time to do the migration. Furthermore, from an infrastructure point of
view, the HOBBIT SDK will make the developing and debugging phase easier, and
the Maven-based framework will facilitate submission. However, another factor behind
the reduced participation in the instance matching tracks lies with their specialization.
New schema matching tracks such as Biodiversity and Ecology typically demand very
little from systems that are already able to tackle long-standing tracks such as Anatomy,
whereas instance matching tracks such as IIMB, Link Discovery and last year’s Process
Model Matching, are so different from one another that each requires dedicated devel-
opment time to tackle. Thus, in future OAEI editions we should consider publishing
new instance matching (and other more specialized) datasets with more time in ad-
vance, to give system developers adequate time to tackle them. Equally critical will be
to ensure stability by maintaining instance matching tracks and datasets over multiple
OAEI editions, so that participants can build upon the development of previous years.
Automatic instance-matching benchmark generation algorithms have been gaining
popularity, as evidenced by the fact that they are used in all three instance matching
tracks of this OAEI edition. One aspect that has not been addressed in such algorithms
is that, if the transformation is too extreme, the correspondence may be unrealistic and
impossible to detect even by humans. As such, we argue that human-in-the-loop tech-
niques can be exploited to do a preventive quality-checking of generated correspon-
dences, and refine the set of correspondences included in the final reference alignment.
We will explore such an approach in future editions of the IIMB track.
The interactive matching track also witnessed a small number of participants,
which have been the same 4 systems over the last three campaigns. This is puzzling
considering that this track is based on the Anatomy and Conference test cases, and
those tracks had 14 participants. The process of programmatically querying the Oracle
class used to simulate user interactions is simple enough that it should not be a deterrent
for participation, but perhaps we should look at facilitating the process further in future
OAEI editions by providing implementation examples.
Finally, the complex matching track opens new perspectives in the field of ontol-
ogy matching, as this is a topic largely unexplored but of growing importance, since
integrating linked datasets often encompasses making complex correspondences. Tack-
ling complex matching automatically is extremely challenging, likely requiring pro-
found adaptations from matching systems, so the fact that there were two participants
able to generate complex correspondences in this track should be seen as a positive
sign of progress to the state of the art in ontology matching. While this year the track
involved different evaluation settings, we will work towards enabling the automatic
evaluation of complex alignments in future editions.
Like in previous OAEI editions, most participants provided a description of their
systems and their experience in the evaluation, in the form of OAEI system papers.
These papers, like the present one, have not been peer reviewed. However, they are full
contributions to this evaluation exercise, reflecting the effort and insight of matching
systems developers, and providing details about those systems and the algorithms they
implement.
The Ontology Alignment Evaluation Initiative will strive to remain a reference to
the ontology matching community by improving both the test cases and the testing
methodology to better reflect actual needs, as well as to promote progress in this field
[41]. 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 are grateful to the Universidad Politécnica de Madrid (UPM), especially to Nan-
dana Mihindukulasooriya and Asunción Gómez Pérez, for moving, setting up and pro-
viding 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 dataset.
We thank Khiat Abderrahmane for his support in the Arabic dataset and Catherine
Comparot for her feedback and support in the MultiFarm test case.
We thank Andrea Turbati and the AGROVOC team for their very appreciated help
with the preparation of the AGROVOC subset ontology. We are also grateful to Cather-
ine Roussey and Nathalie Hernandez for their help on the Taxon alignment.
We also thank for their support the past members of the Ontology Alignment Eval-
uation Initiative steering committee: Jérôme Euzenat (INRIA, FR), Yannis Kalfoglou
(Ricoh laboratories, UK), Miklos Nagy (The Open University,UK), Natasha Noy
(Google Inc., USA), Yuzhong Qu (Southeast University, CN), York Sure (Leib-
niz Gemeinschaft, DE), Jie Tang (Tsinghua University, CN), Heiner Stuckenschmidt
(Mannheim Universität, DE), George Vouros (University of the Aegean, GR).
Cássia Trojahn dos Santos has been partially supported by the French CIMI Labex
project IBLiD (Integration of Big and Linked Data for On-Line Analytics).
Daniel Faria was supported by the ELIXIR-EXCELERATE project (INFRADEV-
3-2015).
Ernesto Jimenez-Ruiz has been partially supported by the BIGMED project (IKT
259055), the SIRIUS Centre for Scalable Data Access (Research Council of Norway,
project no.: 237889), and the AIDA project (UK Government’s Defence & Security
Programme in support of the Alan Turing Institute.
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.
Irini Fundulaki and Tzanina Saveta were supported by the European Union’s Hori-
zon 2020 research and innovation programme under grant agreement No 688227 (Hob-
bit).
Jana Vataščinová and Ondřej Zamazal have been supported by the CSF grant no.
18-23964S.
Patrick Lambrix and Huanyu Li have been supported by the Swedish e-Science
Research Centre and the Swedish National Graduate School in Computer Science
(CUGS).
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