Using Paraphrasers to Detect Duplicities in Ontologies

0580726 - ÚI 2024 RIV PT eng C - Conference Paper (international conference)
Korel, L. - Behr, A. S. - Kockmann, N. - Holeňa, Martin
Using Paraphrasers to Detect Duplicities in Ontologies.
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.), s. 40-49. ISBN 978-989-758-671-2. ISSN 2184-3228.
[KEOD 2023: Conference on Knowledge Engineering and Ontology Development /15./. Rome / hybrid (IT), 13.11.2023-15.11.2023]
Research Infrastructure: ELIXIR CZ III - 90255; e-INFRA CZ II - 90254
Institutional support: RVO:67985807
Keywords : Classifiers * Duplicity Detection * Ontologies * Paraphrasers * Representation Learning * Semantic Similarity
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://doi.org/10.5220/0012164500003598

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.
Permanent Link: https://hdl.handle.net/11104/0349480