STRUCTURAL WEIGHTS IN ONTOLOGY MATCHING
Mohammad Mehdi Keikha1 and Mohammad Ali
Nematbakhsh2 and Behrouz Tork Ladani3
1Computer Science Department, University
of Sistan and Baluchestan, Zahedan, Iran
2,3Computer Engineering Department,
University of Isfahan, Isfahan, Iran
ABSTRACT
Ontology matching finds correspondences between
similar entities of different ontologies. Two ontologies may be similar in some
aspects such as structure, semantic etc. Most ontology matching systems
integrate multiple matchers to extract all the similarities that two ontologies
may have. Thus, we face a major problem to aggregate different similarities.
Some matching systems use experimental weights for aggregation of similarities
among different matchers while others use machine learning approaches and
optimization algorithms to find optimal weights to assign to different
matchers. However, both approaches have their own deficiencies. In this paper,
we will point out the problems and shortcomings of current similarity
aggregation strategies. Then, we propose a new strategy, which enables us to
utilize the structural information of ontologies to get weights of matchers,
for the similarity aggregation task. For achieving this goal, we create a new
Ontology Matching system which it uses three available matchers, namely GMO,
ISub and VDoc. We have tested our similarity aggregation strategy on the OAEI
2012 data set. Experimental results show significant improvements in accuracies
of several cases, especially in matching the classes of ontologies. We will
compare the performance of our similarity aggregation strategy with other
well-known strategies.
Keywords:
Ontology matching, Ontology mapping, Ontology
alignment, Similarity aggregation, Semantic web.
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