Journal
ACM TRANSACTIONS ON MANAGEMENT INFORMATION SYSTEMS
Volume 12, Issue 2, Pages -Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3439772
Keywords
Interactive ontology matching; argumentation framework; collaborative validation
Categories
Funding
- Natural Science Foundation of Fujian Province [2020J01875]
- National Natural Science Foundation of China [61403121]
- Guangxi Key Laboratory of Automatic Detecting Technology and Instruments [YQ20206]
- Program for New Century Excellent Talents in Fujian Province University [GY-Z18155]
- Scientific Research Foundation of Fujian University of Technology [GY-Z17162]
- Science and Technology Planning Project in Fuzhou City [2019-G-40]
- Foreign Cooperation Project in Fujian Province [2019I0019]
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Ontology provides a shared vocabulary of a domain, but different ontologies in the same domain may have heterogeneity issues. It is important to bridge the semantic gap between ontologies through ontology matching techniques to enhance the quality of ontology alignments.
Ontology provides a shared vocabulary of a domain by formally representing the meaning of its concepts, the properties they possess, and the relations among them, which is the state-of-the-art knowledge modeling technique. However, the ontologies in the same domain could differ in conceptual modeling and granularity level, which yields the ontology heterogeneity problem. To enable data and knowledge transfer, share, and reuse between two intelligent systems, it is important to bridge the semantic gap between the ontologies through the ontology matching technique. To optimize the ontology alignment's quality, this article proposes an Interactive Compact Genetic Algorithm (ICGA)-based ontology matching technique, which consists of an automatic ontology matching process based on a Compact Genetic Algorithm (CGA) and a collaborative user validating process based on an argumentation framework. First, CGA is used to automatically match the ontologies, and when it gets stuck in the local optima, the collaborative validation based on the multi-relationship argumentation framework is activated to help CGA jump out of the local optima. In addition, we construct a discrete optimization model to define the ontology matching problem and propose a hybrid similarity measure to calculate two concepts' similarity value. In the experiment, we test the performance of ICGA with the Ontology Alignment Evaluation Initiative's interactive track, and the experimental results show that ICGA can effectively determine the ontology alignments with high quality.
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