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Using Paraphrasers to Detect Duplicities in Ontologies
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SYSNO ASEP 0580726 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název Using Paraphrasers to Detect Duplicities in Ontologies Tvůrce(i) Korel, L. (CZ)
Behr, A. S. (DE)
Kockmann, N. (DE)
Holeňa, Martin (UIVT-O) SAI, RIDZdroj.dok. Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2. - Setubal : SciTePress, 2023 / Aveiro D. ; Dietz J. ; Poggi A. ; Bernardino J. - ISSN 2184-3228 - ISBN 978-989-758-671-2 Rozsah stran s. 40-49 Poč.str. 10 s. Forma vydání Online - E Akce KEOD 2023: Conference on Knowledge Engineering and Ontology Development /15./ Datum konání 13.11.2023 - 15.11.2023 Místo konání Rome / hybrid Země IT - Itálie Typ akce WRD Jazyk dok. eng - angličtina Země vyd. PT - Portugalsko Klíč. slova Classifiers ; Duplicity Detection ; Ontologies ; Paraphrasers ; Representation Learning ; Semantic Similarity Obor OECD Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Výzkumná infrastruktura ELIXIR CZ III - 90255 - Ústav organické chemie a biochemie AV ČR, v. v. i.
e-INFRA CZ II - 90254 - CESNET, zájmové sdružení právnických osobInstitucionální podpora UIVT-O - RVO:67985807 EID SCOPUS 85179547769 DOI 10.5220/0012164500003598 Anotace This paper contains a machine-learning-based approach to detect duplicities in ontologies. Ontologies are formal specifications of shared conceptualizations of application domains. Merging and enhancing ontologies may cause the introduction of duplicities into them. The approach to duplicities proposed in this work presents a solution that does not need manual corrections by domain experts. Source texts consist of short textual descriptions from considered ontologies, which have been extracted and automatically paraphrased to receive pairs of sentences with the same or a very close meaning. The sentences in the received dataset have been embedded into Euclidean vector space. The classification task was to determine whether a given pair of sentence embeddings is semantically equivalent or different. The results have been tested using test sets generated by paraphrases as well as on a small real-world ontology. We also compared solutions by the most similar existing approach, based on GloVe and WordNet, with solutions by our approach. According to all considered metrics, our approach yielded better results than the compared approach. From the results of both experiments, the most suitable for the detection of duplicities in ontologies is the combination of BERT with support vector machines. Finally, we performed an ablation study to validate whether all paraphrasers used to create the training set for the classification were essential. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2024 Elektronická adresa https://doi.org/10.5220/0012164500003598
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