Abstract
Ontology matching is one of the most important work to achieve the goal of the semantic web. To fulfill this task, element-level matching is an indispensable step to obtain the fundamental alignment. In element-level matching process, previous work generally utilizes WordNet to compute the semantic similarities among elements, but WordNet is limited by its coverage. In this paper, we introduce word embeddings to the field of ontology matching. We testified the superiority of word embeddings and presented a hybrid method to incorporate word embeddings into the computation of the semantic similarities among elements. We performed the experiments on the OAEI benchmark, conference track and real-world ontologies. The experimental results show that in element-level matching, word embeddings could achieve better performance than previous methods.
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Zhang, Y. et al. (2014). Ontology Matching with Word Embeddings. In: Sun, M., Liu, Y., Zhao, J. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2014 2014. Lecture Notes in Computer Science(), vol 8801. Springer, Cham. https://doi.org/10.1007/978-3-319-12277-9_4
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DOI: https://doi.org/10.1007/978-3-319-12277-9_4
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