=Paper=
{{Paper
|id=None
|storemode=property
|title=Using concept and structure similarities for ontology integration
|pdfUrl=https://ceur-ws.org/Vol-689/om2010_poster8.pdf
|volume=Vol-689
|dblpUrl=https://dblp.org/rec/conf/semweb/LiuBML10
}}
==Using concept and structure similarities for ontology integration==
<pdf width="1500px">https://ceur-ws.org/Vol-689/om2010_poster8.pdf</pdf>
<pre>
   Using Concept and Structure Similarities for
             Ontology Integration

     Xiulei Liu12 , Payam Barnaghi1 , Klaus Moessner1 , and Jianxin Liao2
        1
          Centre for Communication Systems Research University of Surrey
                    Guildford, Surrey, GU2 7XH, United Kingdom
          2
            State Key Laboratory of Networking and Switching Technology
 Beijing University of Posts and Telecommunications Beijing 100876, P.R. of China
                {xiulei.liu,p.barnaghi,k.moessner}@surrey.ac.uk
                               liaojianxin@ebupt.com


      Abstract. We propose a method to align different ontologies in sim-
      ilar domains and then define correspondence between concepts in two
      different ontologies using the SKOS model.

Introduction. Recently ontologies are created to provide knowledge represen-
tation. They use common representation languages such as OWL, but there are
many heterogeneous ontologies [1–3]. In this paper we first propose a lexical
and structural analysis and compute the concept similarity as a combination
of attributes, second use the SKOS model to define correspondence between
concepts[4].
Ontology Alignment Framework.To perform the matching between concepts
in different ontologies, we focus both on syntactical and text in entity descrip-
tions and also their semantic structure in the ontology representations. This
process, illustrated in the block diagram shown in Figure 1, is divided into two
main sub-tasks: Alignment and SKOS translation. The inputs are two ontologies
and result of the process is an SKOS-based ontology that contains automatically
defined associations.The alignment task analyses lexical and structural attributes
of ontologies to automatically produce associations between concepts. The re-
lation is defined: R(A, B) =< A, B, Relation, S(A, B) > where A and B are
ontology concepts, Relation describe semantic relations between these concepts
which have five types: equal beIncluded, include, disjoint, related, and S(A, B) is
similarity measure for two concepts based on their structure and lexical analysis.




                      Fig. 1. The ontology alignment process
2




       Fig. 2. Snapshot of the specified properties in the integrated ontology


Defining SKOS-based Associations. After identifying possible relations be-
tween concepts, they are imported based on the SKOS model. This will provide
an interconnection between two ontologies based on standard set of properties de-
fined in the SKOS model. The SKOS mapping properties include skos:closeMatch,
skos:exactMatch, skos:broadMatch, skos:narrowMatch and skos:relatedMatch. The
properties maintain a mapping between SKOS concepts adapted from schemes.
The relations in concept pairs defined in the previous section are based on synset
relations in WordNet. They are obtained according to accessing the extended
synset collection for each representative word that describes entities and calcu-
lating structural similarity We will map between synset and SKOS relations. By
applying these mappings, the final product of the ontology integration process
will include assertion axioms in which the related concepts from different ontolo-
gies are linked to each other based on SKOS relations. The integrated ontology
will be a collection of concepts and properties from both ontologies and will
also include the SKOS association properties. Figure 2 illustrates a part of the
SKOS relations and concept alignment between two ontologies from the dataset
(a complete set of our evaluation results using OAEI2008 dataset can be accessed
from: http://tinyurl.com/38veolh).
Acknowledgement.The work is partially supported by the m:Ciudad project
funded by the European 7th Framework Programme, contract number: 215007.
Xiulei Liu’s and Jianxin Liao’s research is supported by Chinese National Science
Fund for Distinguished Young Scholars (No. 60525110) and Chinese National 973
Program (No. 2007CB307100, 2007CB307103).


References
1. T. L. Bach, J. D. Bo, and R. Lara, “Knowledgeweb consortium, state of the art on
   ontology (d2.2.3),” 2004.
2. P.Shvaiko and J.Euzenat, “A survey of schema-based matching approaches,” in J.
   Data Semantics IV, 2005, pp. 146–171.
3. Y. Kalfoglou and M. Schorlemmer, “Ontology mapping: The state of the art,” 2003.
4. A. Miles and S. Bechhofer, “(editors) skos simple knowledge organization
   system reference, w3c recommendation 18 august 2009.” [Online]. Available:
   http://www.w3.org/TR/skos-reference/

</pre>