Supporting ontology maintenance with contextual word embeddings and maximum mean discrepancy
| Autor: | Shroff, N., Vandenbussche, P.-Y., Moore, V., Groth, P., Ben Abbès, S., Hantach, R., Calvez, P., Buscaldi, D., Dessì, D., Dragoni, M., Reforgiato Recupero, D., Sack, H. |
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| Přispěvatelé: | Algorithmic Data Science (IVI, FNWI) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Zdroj: | Joint Proceedings of the 2nd International Workshop on Deep Learning meets Ontologies and Natural Language Processing (DeepOntoNLP 2021) & 6th International Workshop on Explainable Sentiment Mining and Emotion Detection (X-SENTIMENT 2021): co-located with co-located with 18th Extended Semantic Web Conference 2021 : Hersonissos, Greece, June 6th-7th, 2021 (moved online), 11-19 STARTPAGE=11;ENDPAGE=19;TITLE=Joint Proceedings of the 2nd International Workshop on Deep Learning meets Ontologies and Natural Language Processing (DeepOntoNLP 2021) & 6th International Workshop on Explainable Sentiment Mining and Emotion Detection (X-SENTIMENT 2021) |
| Popis: | Ontologies contain an abundance of concepts, are frequently structured as hierarchies, and can cover different domains of knowledge. Polysemous concepts need to be disambiguated for annotation purposes, for example, a concept such as depression has a different meaning in the fields of psychology and economics. In this paper, we introduce the use of the maximum mean discrepancy to indicate whether sets of concepts sharing the same meaning should be merged. This method is a novel approach to ontology maintenance because it provides an objective metric that supports the decision-making of subject matter experts during the concept evaluation process. Our objective is thus to assist ontology maintenance, in particular the organization of concepts, through an analysis framework that gives insights into the polysemy of concepts. |
| Databáze: | OpenAIRE |
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