=Paper=
{{Paper
|id=Vol-225/paper-28
|storemode=property
|title=SOMET: Shared Ontology Matching Environment
|pdfUrl=https://ceur-ws.org/Vol-225/paper28.pdf
|volume=Vol-225
|dblpUrl=https://dblp.org/rec/conf/semweb/Magee06
}}
==SOMET: Shared Ontology Matching Environment==
https://ceur-ws.org/Vol-225/paper28.pdf
SOMET: Shared Ontology Matching
Environment
Liam Magee
Globalism Institute, School of Global Studies, Social Sciences and Planning
RMIT, Melbourne, Australia
liam@commonground.com.au
http://www.rmit.edu.au
Abstract. In this paper we present a tool, SOMET, for collaborative
developing, matching and merging ontologies. The tool’s design is based
on a Wiki model, allowing for multiple authors to contribute to an on-
tology. It also provides a number of meta-ontology features, including
the ability to compare, match and merge. The tool makes use of one
algorithmic approach to element-level mapping, demonstrating the use
of both automated and manual matching.
1 Introduction
Much research has been invested into automated techniques of ontology matching
[6, 7]. There is general recognition of the need to augment these techniques with
manual matching. Collaborative matching, utilising both automated and manual
matches, is important to resolve conceptual ambiguities, and to promote re-use
across organisational and geographical boundaries.
This paper presents an online tool, SOMET (pronounced ‘Summit’), for
collaborative developing, matching and merging ontologies. It is motivated by
the idea that ontology development and matching is essentially a social and
interactive activity, and is best served by tools which permit this. As such, the
tool is based on the Wiki model for ontology development.
This paper is structured as follows: In Section 2, we review related work.
Section 3 presents a case for collaborative ontology development and matching.
Section 4 describes the design and implementation of the SOMET prototype,
along with our approach to ontology matching and merging. Section 5 examines
the test results of using SOMET on two sample ontologies. In Section 6, we
look at further directions for SOMET. Section 7 concludes the paper.
2 Related Work
The concept of shared or collaborative ontology development is not new. A num-
ber of tools have been introduced, including CODE [5], KAON [3], OntoEdit [9],
Ontolingua [4], WebODE [1] and Wiki@nt [2]. SOMET differs from these tools
2 SOMET: Shared Ontology Matching Environment
in focussing particularly on ontology matching and merging in a collaborative
environment.
There has been considerable exploration of approaches for ontology match-
ing. Much of the research has been into finding suitable algorithms to automate
part or all of a given matching task. As shown by surveys [6, 7], such algo-
rithms use a variety of strategies for matching ontologies. This paper explores
the use and partial implementation of one such algorithm, S-MATCH [8]. The
S-MATCH algorithm emphasises its semantic matching characteristics. However
the algorithm also exploits syntactic and external techniques. As such, it is a
good candidate for exploring the use of semi-automated techiques in a collabo-
rative environment.
Recent work also suggests the importance of community-driven ontology
matching [10]. This paper assumes development and matching of ontologies is
often collaborative in nature, and requires tools such as SOMET to realise the
benefit of community-driven domain models.
3 Overview of Collaborative Ontology Development and
Matching
Ontologies are typically developed by a process of iterative construction and
consultation, with a focus on concepts in a specified domain. For the most part
construction and consultation are separate activities, conducted in serial fash-
ion, as the modeller uses specific knowledge of the modelling environment to
apply the results of the consultation process. In the case of traditional database
and software engineering activities, such established practices are generally en-
trenched in broader lifecycle processes, and there has been little impetus to shift
the onus of model construction from the modelling expert. For Semantic Web
ontologies, where models are frequently shared across organisational and geo-
graphical boundaries, there is significantly greater motivation to develop such
models collaboratively.
The approach used in this paper is based on the success of open access
content systems. The approach accepts that a lower grade user interface will be
acceptable in certain contexts, just as authoring content online is an acceptable
degradation from using a word processor in certain contexts. In particular, if
the ontology is small, then the frequent submission and retrieval of ontology
elements across a network of ontology components will be tolerable.
4 Outline of SOMET System, and Matching and Merging
Techniques
SOMET is a prototype that has been developed in Ruby, using the Ruby on
Rails framework. It employs a commonly used model-view-controller architec-
ture. It also employs a plug-in architecture for executing matching algorithms.
SOMET: Shared Ontology Matching Environment 3
The SOMET interface makes it possible to construct an ontology with
classes, properties and individuals. Notable features at this stage include the
following:
– Creation and editing of ontologies, classes, properties, individuals and anno-
tations.
– Importing and exporting an ontology.
– Generation of a comparative report of differences between two ontologies.
– Manual and semi-automated matching of classes.
– Merging of two ontologies.
– Various Wiki features, such as publishing, sharing, publishing, versioning,
logging and commenting on ontologies. Class and property matches can be
proposed, discussed, and approved or rejected.
We have conducted a partial implementation of the S-MATCH algorithm
[8]. As there is not a suitable satisfiability engine in the Ruby language, we
have not been able to implement Step 4 of the algorithm. Consequently we have
not yet been able to test the semantic aspects of S-MATCH, which require the
translation of the labels of the path of a given node on the ontology graph into a
propositional logic formula. Instead we have translated steps 1-3 of S-MATCH to
Ruby, using the OWL object model we have developed. We were able to develop
a matrix of ontology labels with at least partial asssignments of the following
relations: equivalence (=), more general (w), less general (v), and disjointness
(⊥). The result is an element-based, syntactic and external technique, as outlined
in [?] - but without the key semantic characteristics outlined in [8]. The following
outlines the key steps of the algorithm, as presented in [8, 305-7]:
Step 1. For all labels L in the two trees, compute concepts of labels.
Step 2. For all nodes N in the two trees, compute concepts of nodes.
Step 3. For all pairs of labels in the two trees, compute relations among
concepts of labels.
Step 4. For all pairs of nodes in the two trees, compute relations among
concepts of nodes.
For our purposes, we utilised WordNet in steps 1 (to develop the senses of each
lemma) and 3 (to generate the relations between pairs of labels). The generation
of the label matrix at step 3 made use of an exhaustive traversal through the
WordNet dictionary. The results of step 3 are a set of tuples, heID, n1 i, n2 j, Ri,
where eID is a unique identifier for the given element-level match, n1i is the
i-th node of the first ontology graph, n2j is the j-th node of the second ontology
graph, and R is one of equivalence, more general, less general or disjoint. We
ignore possible overlapping or unknown relations. These tuples are captured in
a database, grouped together as an ontology match.
The Merge operation generates a new ontology graph from two existing ones,
on the basis of a defined ontology match. The resulting merged ontology is stored
in the database. The operation has 5 steps:
1. Compute the set of direct child-parent relations for the new graph, based on
the set of element-level matches.
4 SOMET: Shared Ontology Matching Environment
2. Compute the set of direct parent-child relations for the new graph, based on
the results of step 1.
3. Generate the complete set of parent and child nodes for the new graph, based
both on relations from steps 1 and 2, as well as the existing relations in the
source graphs.
4. Compute the set of disjoint relations for the new graph, based on the set
of element-level matches, for all siblings in the graph generated by step 3,
where such siblings are not already disjoint.
5. Perform a deep copy of non-matched objects from each of the source ontolo-
gies into the new ontology.
5 Test Results
The matching and merging capabilities of SOMET have been tested using two
simple ontologies. The goals of the test were to successfully invoke the S-MATCH
implementation, generate a set of class-level matches, add or modify one such
match, and to merge the two ontologies into a third on the basis of the matches.
The test would be successful a) if the merged ontology contained the union
of the two source ontology classes in a directed acyclic graph, with at least
some successful matches, and b) if the Match and Merge operations execute in
reasonable time. Tests were conducted on a P4 3.0GHz machine with 2GB of
memory.
The matching operation took 74.407 seconds. Further analysis showed the
majority of this time was due to the exhaustive scansion of the WordNet database.
The merging operation took 5.703 seconds. The results show some inconsisten-
cies of the WordNet associations, with certain anomalous subsumtion relations
identified.
The results of the test show that for small ontologies, the Match and Merge
operations can be conducted in an shared online environment. While the per-
formance is sub-optimal, this could be corrected by depth-limited WordNet
searches, and augmented with domain-specific vocabularies. The quality of the
match also varies, although implementing step 4 of the S-MATCH algorithm,
ie. the satisfiability checks on formulas representing the full path of any given
element, would improve this greatly. The test also demonstrates the ability to
augment automated matching techniques with manual matching.
6 Further Work
SOMET has been developed to a prototype level, and as such lacks the kinds
of user interface enhancements expected of such a tool in a production environ-
ment. To realise the aims of providing a generic matching tool with ‘pluggable’
matching techniques, a more sophisticated plug-in architecture needs to be de-
veloped. Given the diversity of matching approaches, the viability of this aim
also needs to be better ascertained.
SOMET: Shared Ontology Matching Environment 5
7 Conclusions
Research on publishing and document standards over several years has motivated
the investigation into how ontologies can be developed and matched in a collab-
orative way. We have concluded social interaction, negotiation and collaboration
are necessary aspects to successful ontology matching in many environments. So
far, tools for ontology matching have focussed on private ontology matching. In
this paper we have presented SOMET as a prototype for collaborative ontology
development, matching and merging. The test results have been encouraging in
terms of its utility for these purposes.
Acknowledgments. This work has been supported by ARC Grant LP0667834,
‘Towards the Semantic Web: Standards and Interoperability across Document
Management and Publishing Supply Chains’.
References
1. J. C. Arpirez, O. Corcho, M. Fernandez-Lopez, and A. Gomez-Perez. Webode: a
scalable workbench for ontological engineering. In Proceedings of the international
conference on Knowledge capture, pages 6-13. ACM Press, 2001.
2. A. Bao, V. Honavar. Collaborative Ontology Building with Wiki@nt - A Multi-
agent Based Ontology Building Environment. In Proceedings of the 3rd International
Workshop on Evaluation of Ontology-based Tools (ISWC 2004). Technical Report,
Computer Science, Iowa State University.
3. E. Bozsak, M. Ehrig, S. Handschuh, A. Hotho, A. Maedche, B. Motik, D. Oberle, C.
Schmitz, S. Staab, L. Stojanovic, N. Stojanovic, R. Studer, G. Stumme, Y. Sure, J.
Tane, R. Volz, and V. Zacharias. Kaon - towards a large scale semantic web. In K.
Bauknecht, A. M. Tjoa, and G. Quirchmayr, editors, E-Commerce and Web Tech-
nologies, Third International Conference, EC-Web 2002, Aix-en-Provence, France,
September 2-6, 2002, Proceedings, volume 2455 of Lecture Notes in Computer Sci-
ence, pages 304-313. Springer, 2002.
4. A. Farquhar, R. Fikes, W. Pratt, and J. Rice. Collaborative ontology construction
for information integration, 1995.
5. P. Hayes, R. Saavedra, and T. Reichherzer. A collaboration development environ-
ment for ontologies. In Proceedings of the Semantic Integration Workshop, Sanibel
Island, Florida, 2003.
6. N. Noy. Semantic Integration. A Survey of Ontology-based Approaches. Sigmod
Record, Special Issue on Semantic Integration, 2004.
7. P. Shvaiko, J. Euzenat. Survey of Schema-based Matching Approaches. Journal on
Data Semantics, 2005.
8. P. Shvaiko, F. Giunchiglia, P. Pinheiro da Silva, D. L. McGuinness. Web Explana-
tions for Semantic Heterogeneity Discovery. In Proceedings of ESWC, 2005.
9. Y. Sure, M. Erdmann, J. Angele, S. Staab, R. Studer, and D. Wenke. OntoEdit:
Collaborative ontology development for the semantic web. In Proceedings of the
First International Semantic Web Conference 2002 (ISWC 2002), June 9-12 2002,
Sardinia, Italia. Springer, LNCS 2342, 2002.
10. A. Zhdanova, P. Shvaiko. Community-Driven Ontology Matching. In Proceedings
of ESWC, 2006.