Abstract
The ontology matching technique aims at solving the ontology heterogeneity problem by finding the semantically identical entities between two ontologies. Modeling ontologies in vector space and using similarity measures based on vector space to calculate two entities’ similarity is an effective method. In this work, a Word2Vec based ontology matching technique (W2V-OM) is proposed to calculate entities’ similarity, and the Wikipedia training data are used to improve the generalizability of the model and the accuracy of alignments. In the experiment, the performance of W2V-OM is tested with the benchmark track provided by Ontology Alignment Evaluation Initiative (OAEI). The experimental results show that W2V-OM can determine better alignments than OAEI’s participants.
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Acknowledgment
This work is supported by Fujian Province 13th Five-Year Plan teaching Reform Project in 2019 (No. FBJG20190156), the third batch of key lifelong education projects in Fujian province (No. ZS20033), the Research Innovation Team of Concord University College Fujian Normal University in 2020 (No. 2020-TD-001), the 2018 Program for Outstanding Young Scientific Researcher in Fujian Province University and Scientific Research Project of Concord University College of Fujian Normal University in 2020 (No. KY20200203).
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Liao, J., Huang, Y., Wang, H., Li, M. (2021). Matching Ontologies with Word2Vec Model Based on Cosine Similarity. In: Hassanien, A.E., et al. Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021). AICV 2021. Advances in Intelligent Systems and Computing, vol 1377. Springer, Cham. https://doi.org/10.1007/978-3-030-76346-6_34
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