Abstract
Grouping objects into a common, initially unknown, category underlies several important tasks, such as query suggestion or automatic lexicon generation. However, while coming up with more things “of the same kind” is easy for humans, it is not trivial for Artificial Intelligence. This task is commonly known as the Entity Set Expansion (ESE) problem, and has been studied in different branches of AI and NLP. In this paper, we review different similarity metrics and techniques that could be applied to the ESE problem. Moreover, we decompose the problem into phases and demonstrate how to use several approaches together. In particular, we combine semantic similarity metrics with Meta Path algorithm for knowledge graphs. We discuss the results and show that the presented setting can be reused in further research into hybrid approaches to the ESE problem.
This paper is supported by AGH UST.
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Adrian, W.T., Wilk, K., Adrian, M., Kluza, K., Ligęza, A. (2022). Semantic Similarity Analysis for Entity Set Expansion. In: Fred, A., Aveiro, D., Dietz, J., Salgado, A., Bernardino, J., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2020. Communications in Computer and Information Science, vol 1608. Springer, Cham. https://doi.org/10.1007/978-3-031-14602-2_3
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