A New Ontology Meta-Matching Technique with a Hybrid Semantic Similarity Measure
Ontology is the kernel technique of semantic web, which can be used to describe the concepts and their relationships in a particular domain. However, different domain experts would construct the ontologies according to different requirements, and there exists a heterogeneity problem among the ontologies, which hinders the interaction between ontology-based intelligent systems. Ontology matching technique can determine the links between heterogeneous concepts, which is an effective method for solving this problem. Semantic similarity measure is a function to calculate to what extent two concepts are similar to each other, which is the key component of ontology matching technique. Generally, multiple semantic similarity measures are used together to improve the accuracy of the concept recognition. How to combine these semantic similarity measures, i.e., the ontology meta-matching problem, is a challenge in the ontology matching domain. To address this challenge, this paper proposes a new ontology meta-matching technique, which applies a novel combination framework to aggregate two broad categories of similarity measures. The experiment uses the famous benchmark provided by the Ontology Alignment Evaluation Initiative (OAEI). Comparing results with the participants of OAEI shows the effectiveness of the proposal.
KeywordsOntology meta-matching Semantic similarity measure OAEI
This work is supported by the Program for New Century Excellent Talents in Fujian Province University (No. GY-Z18155), the Program for Outstanding Young Scientific Researcher in Fujian Province University (No. GY-Z160149), the 2018 Program for Outstanding Young Scientific Researcher in Fujian, the Scientific Research Project on Education for Young and Middle-aged Teachers in Fujian Province (No. JZ170367), and the Scientific Research Foundation of Fujian University of Technology (No. GY-Z17162).
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