Number of the records: 1  

Using Paraphrasers to Detect Duplicities in Ontologies

  1. 1.
    SYSNO ASEP0580726
    Document TypeC - Proceedings Paper (int. conf.)
    R&D Document TypeConference Paper
    TitleUsing Paraphrasers to Detect Duplicities in Ontologies
    Author(s) Korel, L. (CZ)
    Behr, A. S. (DE)
    Kockmann, N. (DE)
    Holeňa, Martin (UIVT-O) SAI, RID
    Source TitleProceedings 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
    Pagess. 40-49
    Number of pages10 s.
    Publication formOnline - E
    ActionKEOD 2023: Conference on Knowledge Engineering and Ontology Development /15./
    Event date13.11.2023 - 15.11.2023
    VEvent locationRome / hybrid
    CountryIT - Italy
    Event typeWRD
    Languageeng - English
    CountryPT - Portugal
    KeywordsClassifiers ; Duplicity Detection ; Ontologies ; Paraphrasers ; Representation Learning ; Semantic Similarity
    OECD categoryComputer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
    Research InfrastructureELIXIR CZ III - 90255 - Ústav organické chemie a biochemie AV ČR, v. v. i.
    e-INFRA CZ II - 90254 - CESNET, zájmové sdružení právnických osob
    Institutional supportUIVT-O - RVO:67985807
    EID SCOPUS85179547769
    DOI10.5220/0012164500003598
    AnnotationThis 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.
    WorkplaceInstitute of Computer Science
    ContactTereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800
    Year of Publishing2024
    Electronic addresshttps://doi.org/10.5220/0012164500003598
Number of the records: 1  

  This site uses cookies to make them easier to browse. Learn more about how we use cookies.