=Paper=
{{Paper
|id=Vol-551/paper-14
|storemode=property
|title=KOSIMap: ontology alignments results for OAEI 2009
|pdfUrl=https://ceur-ws.org/Vol-551/oaei09_paper7.pdf
|volume=Vol-551
|dblpUrl=https://dblp.org/rec/conf/semweb/ReulP08
}}
==KOSIMap: ontology alignments results for OAEI 2009==
https://ceur-ws.org/Vol-551/oaei09_paper7.pdf
KOSIMap: Ontology alignments results for
OAEI 2009
Quentin Reul1 and Jeff Z. Pan2
1
VUB STARLab, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
2
University of Aberdeen, Aberdeen AB24 3FX, UK
Quentin.Reul@vub.ac.be; jeff.z.pan@abdn.ac.uk
Abstract. Ontology mapping has been recognised as an important ap-
proach to identifying similar information in heterogeneous ontologies.
The Knowledge Organisation System Implicit Mapping (KOSIMap) ap-
proach relies on DL reasoning (i) to extract background knowledge about
every entity, and (ii) to remove inappropriate correspondences from an
alignment. The main assumption is that the use of this background
knowledge reduces erroneous mappings, thus increasing coverage. In this
paper, we provide an overview of KOSIMap, and present the result of our
system for its first participation to the Ontology Alignment Evaluation
Initiative (OAEI).
1 Presentation of KOSIMap
Ontology mapping has been recognised as an important means to identify sim-
ilar information in different ontologies, thus achieving semantic interoperability
on the Web. Given two ontologies O1 and O2 , the task of mapping one ontol-
ogy to another is that of finding an entity in O1 that matches an entity in O2
based on their intended meaning. Many approaches to schema/ontology match-
ing have been proposed over the years [5, 7, 10]. Furthermore, surveys reviewing
these approaches, techniques and tools have been provided [4, 1]. The Knowledge
Organisation System Implicit Mapping (KOSIMap) approach differs from exist-
ing approaches by relying on DL reasoning (i) to extract background knowledge
about every entity, and (ii) to remove inappropriate correspondences from an
alignment.
1.1 State, Purpose, General Statement
KOSIMap is an extensional and asymmetric matching approach implemented
in Java. Given two consistent ontologies, KOSIMap aligns entities in the source
ontology to entities in the target ontology by extracting background knowledge
about entities based on DL reasoning. More specifically, a DL reasoner (e.g.
FaCT++ [12], Pellet [9]) deduces logical consequences about an entity based on
the asserted axioms defined in an ontology. Moreover, we investigate the use of
DL reasoning to remove inappropriate correspondences from an alignment. The
main assumption is that the use of these logical consequences reduces erroneous
mappings, thus increasing coverage.
The current KOSIMap implementation of produces a set of homogeneous cor-
respondences, where classes are mapped to classes. object properties to object
properties, and datatype properties to datatype properties. More specifically,
the approach computes the similarity between two entities based on their re-
spective sets of features (e.g. subsumption). Note that KOSIMap only considers
the equivalence mapping relation between two entities.
1.2 Specific Techniques Used
The KOSIMap system calculates the similarity between entities for a pair of
ontologies by analysing three features; namely lexical description (i.e. label), hi-
erarchical structure (subsumers for concepts, and super-properties), and internal
structure (inherited properties for classes, domains and ranges for object proper-
ties, and domains for datatype properties). The measures obtained by comparing
these three features are then combined into a single value using a weighted sum
in a similar manner to [2]. These weights are set by a user depending on the
input ontologies, and requirements for the output.
Fig. 1. The architecture of KOSIMap.
Figure 1 shows the architecture of our approach. KOSIMap consists of three
main steps; namely Pre-Processing, Similarity Generation, and Alignment Ex-
traction. The pre-processing step includes three sub-tasks. It first parses the
two ontologies with the OWL API [6]. The OWL API provides an interface
to access the explicit information for each entity defined in an ontology. The
API supports several representations including XML/RDF, KRSS and OBO
flat files. Secondly, natural language techniques (i.e. elimination, lemmatization,
and transformation) are applied to each entity to obtain their most basic form.
Entities are not only defined by annotation properties, but also by the semantics
provided by the axioms in the ontology. Thus, the final pre-processing sub-task
extracts logical consequences (i.e. background information) resulting from as-
serted axioms. The current implementation uses the FaCT++ API3 to classify
the different ontologies.
Definition 1 (Degree of Commonality Coefficient). Given two sets Ss and
St , the degree of commonality coefficient between them, denoted DoCCoeff(Ss , St )
is defined as:
1 ∑
DoCCoeff(Ss , St ) = max sim(ei , ej ) (1)
max(|Ss |, |St |) ej ∈St
ei ∈Ss
where Ss is the source set, St is the target set, and sim(ei , ej ) computes the
similarity between pair of elements in the two sets.
Secondly, the similarity generator computes three kinds of similarities; namely
syntax similarity, property-based similarity, and class-based similarity. The most
basic feature of entities is their labels. Labels are human identifiers (i.e. words)
expressed in a vocabulary shared by experts in the same domain. Therefore, we
assume that equivalent classes are likely to be modelled using similar labels (or
names). KOSIMap relies on string similarity (e.g. Jaro-Winkler [14], Q-Gram
[11], Monge-Elkan [8], and SMOA [10]) to calculate the label similarity for each
pair of entities. The SimMetrics API4 provides a library of normalised and op-
timised similarity (or distance) metrics. The property-based similarity and the
class-based similarity both rely on the degree of commonality coefficient (Def-
inition 1) to provide an similarity value between two sets of complex objects.
The property-based similarity focuses on features containing properties (i.e. set
of super-properties for OWLObjectProperty and OWLDataProperty and the set
of inherited properties for OWLClass), while the class-based similarity focuses
on features containing classes (i.e. set of subsumers for OWLClass and the set
of binary relation containing their domain and range for OWLObjectProperty).
The results of the different similarity approaches are then aggregated for each
pair of entities and stored into a n*m matrix, where is the number of element
in the source ontology and m is the number of elements in the target ontology.
The aggregated similarity score for a pair of entities is obtained by applying a
weighted function (see Equation 2), where the weights (i.e. wk ) for each measure
3
http://code.google.com/p/factplusplus/
4
http://sourceforge.net/projects/simmetrics/
is in the range [0,1] and their total is 1.
∑
n
sim(e1 , e2 ) = wk simk (e1 , e2 ) (2)
k=0
After the similarity aggregation, we have a n*m matrix containing pairs of
entities with a similarity value. The problem is to extract a set of relevant map-
pings from the matrix. This is normally achieved by discarding all candidate
mappings below a threshold ζ. However, this method may return multiple map-
pings for each entity in the source ontology. In KOSIMap, we follow a two-step
approach to extract mappings. First, the approach extracts a pre-alignment from
the matrix, by selecting the maximum similarity score for each row in the matrix
(i.e. for each n). This pre-alignment is then passed through a refinement process,
which eliminates inappropriate mappings. In KOSIMap, we use DL reasoning to
extract the local implication as part of the mapping extraction process. This
approach extends the work by Wang and Xu [13], which only checked whether
local implications were asserted in an ontology. As our approach only supports
equivalent mapping relations, we focus on removing inconsistent mappings from
the pre-alignment. Inconsistent mappings occur when the local consistency of
an ontology is violated by the introduction of a correspondence between two
ontologies. For example, an local inconsistency would occur if several entities in
the source ontology are mapped to the same entity in the target ontology, and
that the two classes are not recognised as equivalent by a DL reasoner.
1.3 Adaptations Made for the Evaluation
As stated in Section 1.1, KOSIMap is an asymmetric matching approach. The
asymmetricity results from Equation 1, which consider the maximum value for
each element in the source set. However, the organisers of OAEI campaign re-
quested that we delivered a symmetric set of alignments. As a result, we modify
the similarity generation for the property-based and class-based similarity to
consider the biggest set as the source set. Moreover, we implemented a Java
class to run the different tracks in batch mode. Moreover, the parameters taken
by the approach (i.e. weights and thresholds) were tuned and set depending on
the type of information contained in the ontologies to be mapped. For example,
the property-based similarity was not calculated for the directory track as no
properties were defined.
1.4 Link to the Set of Provided Alignments (in align format)
The results of the 2009 OAEI campaign for the KOSIMap system can be found
at http://www.csd.abdn.ac.uk/~qreul/research/OAEI2009.zip.
2 Results
In this section, we present the results of the 2009 OAEI campaign obtained
by the KOSIMap system. KOSIMap was used to generate alignments for four
tracks, namely benchmark, anatomy, conference and directory. Note that the full
results of the Alignment Evaluation Initiative (OAEI) 2009 Campaign can be
found in [3]. The experiments were carried on a Mac Book with an Intel Core
2 Duo processor (2.13GHz) and 4GB RAM running Mac OSX. The minimum
memory for the Java Virtual Machine was set to 512MB, while its maximum was
set to 1GB. In this experiment, we used FaCT++ as the default DL reasoner
unless stated otherwise.
2.1 Benchmark
The benchmark data set contains 111 alignment tasks. KOSIMap follows the
approach defined in Section 1.2. In this experiment, we used the Q-Gram sim-
ilarity measure to compute the syntax similarity and as the similarity function
for the degree of commonality coefficient. The FaCT++ reasoner returned an ex-
ception ( NonSimpleRoleInNumberRestrictionException) for some tests (i.e.
222, 230, 237, 251, 258, and 304), so we used the Pellet reasoner for this test.
The threshold was set to 0.2, while the weights were set as follows:
– Weight for syntax similarity: 0.3
– Weight for property-based similarity: 0.2
– Weight for class-based similarity: 0.5
KOSIMap gets near perfect alignment (Precision and Recall is 0.99) for tests
101, 103 and 104 (Table 1). Although KOSIMap performs quite well in the
2xx tests, it yields very low recall (≤ 0.1) when labels in the target ontology
have been scrambled (i.e. tests #202 #248, #249, #25x, and #26x). Note that
KOSIMap yields high recall (i.e. ≥ 0.9) for tests #221 to #247. For the real
ontology data set (i.e. 3xx), KOSIMap yields 0.815 for Precision and .425 for
Recall. Finally, KOSIMap achieves a much better harmonic mean precision than
edna even though our system yields the same recall.
Table 1. Results for KOSIMap at the OAEI 2009 campaign for the benchmark test case.
Tool KOSIMap edna
Test Prec. Rec. Prec. Rec.
#1xx 0.99 0.99 0.96 1.0
#2xx 0.94 0.57 0.41 0.56
#3xx 0.72 0.50 0.47 0.82
H-Mean 0.91 0.59 0.43 0.59
2.2 Anatomy
The anatomy data set consists of two large scale anatomy ontologies. On the one
hand, the Adult Mouse Anatomical Dictionary5 represents the anatomical struc-
ture of the postnatal mouse and contains 2744 classes organised hierarchically
5
http://www.informatics.jax.org/searches/AMA_form.shtml
by “is-a” and “part-of” relationships. On the other hand, the NCI Thesaurus6
is a reference terminology and biomedical ontology covering clinical care, trans-
lational and basic research, public information, and administrative activities.
This ontology contains a subset of the classes defined in the thesaurus (i.e. 3304
classes). Note that the property-based similarity was discarded for this track as
these ontologies only contain a very small number of properties.
KOSIMap produces an alignment for three of the four sub-tasks of this track:
1. Optimal solution: The optimal solution is obtained with a threshold set to
0.6, the syntax similarity set to 0.6 and the class-based similarity set to 0.4.
It took KOSIMap approximately 5 min to generate the alignment.
2. Optimal precision: The optimal solution is obtained with a threshold set to
0.7, the syntax similarity set to 0.6 and the class-based similarity set to 0.4.
It took KOSIMap approximately 5 min to generate the alignment.
3. Optimal recall : The optimal solution is obtained with a threshold set to 0.6,
the syntax similarity set to 0.6 and the class-based similarity set to 0.4. It
took KOSIMap approximately 5 min to generate the alignment.
Table 2. Results for KOSIMap at the OAEI 2009 campaign for the anatomy test case.
Tool Optimal solution Optimal precision Optimal recall
Runtime Prec. Rec. FMeas. Prec. Rec. FMeas. Prec. Rec. FMeas.
KOSIMap ≈ 5 min 0.87 0.62 0.72 0.91 0.45 0.60 0.87 0.62 0.72
The results of the anatomy track are shown in Table 2. KOSIMap takes
around 5 minutes to extract mappings between the Adult Mouse Anatomical
Dictionary and the NCI Thesaurus and the F-Measure is 0.72. We observer
significant differences with regard to the trade-off between precision and recall.
For instance, we observe that the recall obtained by KOSIMap falls from 0.62 to
0.45 when generating an alignment for optimal precision sub-task. As KOSIMap
favours recall over precision, the score obtained for the optimal recall sub-task
is the same as the optimal solution.
2.3 Conference
This track contains 15 ontologies covering the conference organization domain.
These ontologies differ in terms of DL expressivity and size. For example, ekaw.owl
is represented in SHIN , while paperdyne.owl is expressed in ALCHIN (D).
KOSIMap generated 105 non-empty alignments with parameters set as fol-
lows:
– Weight for syntax similarity: 0.3
– Weight for property-based similarity: 0.2
– Weight for class-based similarity: 0.5
6
http://www.cancer.gov/cancerinfo/terminologyresources/
Table 3 shows the precision, recall, and F-measure computed for three dif-
ferent thresholds (0.2, 0.5, and 0.7). The results show that KOSIMap reaches
an optimal solution with the threshold set to 0.5 before obtaining lower perfor-
mances with higher thresholds. Moreover, the precision achieved by our system
increases at the same time as the threshold.
Table 3. Results for KOSIMap at the OAEI 2009 campaign for the conference test case.
Tool threshold=0.2 threshold=0.5 threshold=0.7
Prec. Rec. FMeas. Prec. Rec. FMeas. Prec. Rec. FMeas.
KOSIMap 0.18 0.56 0.27 0.41 0.43 0.41 0.70 0.23 0.33
2.4 Directory
The directory track consists of 4639 test cases. As no properties (object proper-
ties or datatype properties) are found in this track, the property-based similarity
is discarded for this track. In this experiment, the threshold is set to .0, while
the weights are set to 0.6 (for syntax similarity) and 0.4 (for the class-based
similarity). Due of the low expressivity of the ontologies (i.e. AL), we simplified
the rules to retain the correspondence with the highest score when a class in
the source ontology maps to several classes in the target ontology. KOSIMap
takes just over 1 minute to generate the 4639 alignments. The preliminary re-
sults of this track yielded a score of 0.618 for Precision, 0.453 for Recall, and a
F-Measure of 0.523.
3 General Comments
3.1 Comments on the Results
From the results we can see that KOSIMap can take advantage of all different
features associated with entities. The lexical description is especially important
to achieve high precision and recall, while the hierarchical and internal structure
are used to refine the final alignment. For example, tests in the benchmark track
with scrambled labels (i.e. tests 248 to 266) tend to yield very low recall.
Based on the anatomy track, we have demonstrated the scalability of our
approach. Although the two ontologies are not very expressive (i.e. ALE for
AMA and ALE+ for the NCI thesaurus), we have shown that the use of a
DL reasoner does not impact the scalability of our system. Thus, this result
suggests that the use of a reasoner does not greatly increase the runtime of the
mapping task. Note that testing on more expressive large-scale ontologies should
be carried to further test this observation.
3.2 Discussions on the Way to Improve KOSIMap
KOSIMap uses different strategies to extract correspondences between two on-
tologies. Based on the test library, we have seen that some strategies (e.g.
property-based similarity) were not always useful to extract alignments. One
possible way to improve the current system would be to include a strategy se-
lection module. With strategy selection, KOSIMap could avoid some some noise
produced by some strategies when the information these strategies rely on is not
adequate. For example, when no properties are defined in the ontology.
Another improvement to the system would be to include a module to fine-tune
weights when combining the different similarity measure. The current approach
relies on the user to analyse the information contained in the ontologies. It is
important to note that this is process is both time-consuming and error prone.
A solution to this problem would be to consider the DL expressivity of both
ontologies to analyse the impact of each measure on the global similarity value.
3.3 Comments on the OAEI 2009 Test Cases
The advantage of the OAEI test library is that it provides a wide range of tests
covering real word and modied ontologies. For example, the benchmark track
allows anyone to clearly identify the strengths and weaknesses of their systems.
The library also includes test cases for comparing large scale ontologies. However,
the ontologies provided in the anatomy track are not very expressive. As a result,
it is difficult to address the impact of using DL reasoners on large scale ontologies.
4 Conclusion
In this paper, we present the KOSIMap system, which aligns entities from two
ontologies. This system relies on DL reasoning to (i) extract background knowl-
edge about every entity, and (ii) to remove inappropriate correspondences from
an alignment. KOSIMap consists of three main steps; namely Pre-Processing,
Similarity Generation, and Alignment Extraction. It first parses the two on-
tologies, extracts the implicit structure of both ontologies using an OWL DL
reasoner, and applies natural language techniques to lexical descriptions (i.e.
labels). Next, it computes three different types of similarities for every pair of
entities. These similarity values are then combined and stored in a n*m ma-
trix from which a pre-alignment is extracted. This pre-alignment is then passed
through a refinement process, which eliminates inconsistent mappings.
Secondly, we report the results obtained by KOSIMap for its first partic-
ipation to the Ontology Alignment Evaluation Initiative. From the results of
the benchmark test case, we can see that our system can take advantage of
all different features associated with entities during the ontology mapping task.
We have also shown that KOSIMap remains scalable despite using DL reasoning
throughout the mapping process. However, testing on more expressive large-scale
ontologies should be carried to further test this observation.
5 Acknowledgements
The IPAS project is co-funded by the Technology Strategy Board’s Collaborative
Research and Development programme (www.innovateuk.org) and Rolls-Royce
(project No. TP/2/IC/6/I/10292).
References
1. S. Castano, A. Ferrara, S. Montanelli, G. N. Hess, and S. Bruno. State of the art
on ontology coordination and matching. Report FP6-027538, BOEMIE, March
2007.
2. M. Ehrig and Y. Sure. Ontology mapping - an integrated approach. In Proceedings
of the 1st European Semantic Web Symposium (ESWS 04), pages 76–91, 2004.
3. J. Euzenat, A. Ferrara, L. Hollink, V. Malaise, C. Meilicke, A. Nikolov, J. Pane,
F. Scharffe, P. Shvaiko, V. Spiliopoulos, H. Stuckenschmidt, O. Svab-Zamazal,
V. Svatek, C. T. dos Santos, and G. Vouros. Preliminary results of the ontol-
ogy alignment evaluation initiative 2009. In Proceedings of the 4th International
Workshop on Ontology Matching (OM-2009), 2009.
4. J. Euzenat and P. Shvaiko. Ontology Matching. Springer-Verlag, Berlin, 2007.
5. J. Euzenat and P. Valtchev. An integrative proximity measure for ontology align-
ment. In Proceedings of ISWC-2003 Workshop on Semantic Information Integra-
tion, page 3338, 2003.
6. M. Horridge, S. Bechhofer, and O. Noppens. Igniting the OWL 1.1 touch pa-
per: The OWL API. In Proceedings of the 3rd OWL Experienced and Directions
Workshop (OWLED 2007), Innsbruck, Austria, 2007.
7. A. Maedche and S. Staab. Measuring similarity between ontologies. In Proceedings
of the 13th International Conference on Knowledge Engineering and Knowledge
Management (EKAW 2002), pages 251–263, Siguenza, Spain, 2002.
8. A. E. Monge and C. Elkan. The field matching problem: Algorithms and applica-
tions. In Proceedings of the 2nd International Conference on Knowledge Discovery
and Data Mining, 1996.
9. E. Sirin, B. Parsia, B. C. Grau, A. Kalyanpur, and Y. Katz. Pellet: A practical
OWL-DL reasoner. Web Semantics: Science, Services and Agents on the World
Wide Web, 5(2):51–53, June 2007.
10. G. Stoilos, G. Stamou, and S. Kollias. A string metric for ontology alignment. In
Proceeding of the 4th International Semantic Web Conference (ISWC 2005), 2005.
11. E. Sutinen and J. Tarhio. On using Q-Gram locations in approximate string match-
ing. In Proceedings of the 3rd Annual European Symposium on Algorithms (ESA
95), page 327340, 1995.
12. D. Tsarkov and I. Horrocks. FaCT++ description logic reasoner: System descrip-
tion. In Proceedings of the 3rd International Joint Conference on Automated Rea-
soning (IJCAR 2006), pages 292–297, Seattle, USA, August 2006.
13. P. Wang and B. Xu. Debugging ontology mappings: A static approach. Computing
and Informatics, 22:1001–1015, 2003.
14. W. E. Winkler. String comparator metrics and enhanced decision rules in the
Fellegi-Sunter Model of record linkage. In Proceedings of the Section on Survey
Research Methods, pages 354–359. American Statistical Association, 1990.