Szczegóły publikacji
Opis bibliograficzny
Semantic similarity analysis for entity set expansion / Weronika T. ADRIAN, Kornel Wilk, Marek ADRIAN, Krzysztof KLUZA, Antoni LIGĘZA // W: Knowledge Discovery, Knowledge Engineering and Knowledge Management : 12th International Joint Conference, IC3K 2020 : virtual event, November 2–4, 2020 : revised selected papers / eds. Ana Fred, [et al.]. — Cham : Springer Nature Switzerland, cop. 2022. — (Communications in Computer and Information Science ; ISSN 1865-0929 ; vol. 1608). — ISBN: 978-3-031-14601-5; e-ISBN: 978-3-031-14602-2. — S. 45–69. — Bibliogr., Abstr. — Publikacja dostępna online od: 2022-09-07
Autorzy (5)
Słowa kluczowe
Dane bibliometryczne
ID BaDAP | 143081 |
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Data dodania do BaDAP | 2022-10-14 |
DOI | 10.1007/978-3-031-14602-2_3 |
Rok publikacji | 2022 |
Typ publikacji | materiały konferencyjne (aut.) |
Otwarty dostęp | |
Wydawca | Springer |
Konferencja | 12th international joint conference on knowledge discovery, knowledge engineering and knowledge management |
Czasopismo/seria | Communications in Computer and Information Science |
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.