Mathematics (Jan 2023)
Similarity Feature Construction for Matching Ontologies through Adaptively Aggregating Artificial Neural Networks
Abstract
Ontology is the kernel technique of Semantic Web (SW), which enables the interaction and cooperation among different intelligent applications. However, with the rapid development of ontologies, their heterogeneity issue becomes more and more serious, which hampers communications among those intelligent systems built upon them. Finding the heterogeneous entities between two ontologies, i.e., ontology matching, is an effective method of solving ontology heterogeneity problems. When matching two ontologies, it is critical to construct the entity pair’s similarity feature by comprehensively taking into consideration various similarity features, so that the identical entities can be distinguished. Due to the ability of learning complex calculating model, recently, Artificial Neural Network (ANN) is a popular method of constructing similarity features for matching ontologies. The existing ANNs construct the similarity feature in a single perspective, which could not ensure its effectiveness under diverse heterogeneous contexts. To construct an accurate similarity feature for each entity pair, in this work, we propose an adaptive aggregating method of combining different ANNs. In particular, we first propose a context-based ANN and syntax-based ANN to respectively construct two similarity feature matrices, which are then adaptively integrated to obtain a final similarity feature matrix through the Ordered Weighted Averaging (OWA) and Analytic hierarchy process (AHP). Ontology Alignment Evaluation Initiative (OAEI)’s benchmark and anatomy track are used to verify the effectiveness of our method. The experimental results show that our approach’s results are better than single ANN-based ontology matching techniques and state-of-the-art ontology matching techniques.
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