Authors:
Lukáš Korel
1
;
Alexander Behr
2
;
Norbert Kockmann
2
and
Martin Holeňa
1
;
3
Affiliations:
1
Faculty of Information Technology, Czech Technical University, Prague, Czech Republic
;
2
Faculty of Biochemical and Chemical Engineering, TU Dortmund University, Dortmund, Germany
;
3
Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic
Keyword(s):
Ontologies, Semantic Similarity, Duplicity Detection, Representation Learning, Paraphrasers, Classifiers.
Abstract:
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.
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