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Erschienen in:
2018 | OriginalPaper | Buchkapitel
A Large Scale Multi-objective Ontology Matching Framework
verfasst von : Xingsi Xue, Aihong Ren
Erschienen in: Advances in Intelligent Information Hiding and Multimedia Signal Processing
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Abstract
Multi-Objective Evolutionary Algorithm (MOEA) is emerging as a state-of-the-art methodology to solve the ontology meta-matching problem. However, the huge search scale of large scale ontology matching problem stops MOEA based ontology matching technology from correctly and completely identifying the semantic correspondences. To this end, in this paper, a large scale multi-objective ontology matching framework is proposed, which works with three sequential steps: (1) partition the large scale ontologies into similar ontology segment pairs; (2) utilize MOEA to match the similar ontology segments in parallel; (3) select the representative ontology segment alignments, which are further aggregated to obtain the final ontology alignment. In addition, a novel multi-objective model is also constructed for ontology matching problem and the MOEA and entity similarity measure that could be used in this framework are also recommended. The experimental result shows the effectiveness of our proposal.