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Applying of machine learning techniques to combine string-based, language-based and structure-based similarity measures for ontology matchingстатья
- Авторы: Bulygin L., Stupnikov S.
- Сборник: Selected Papers of the XXI International Conference on Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2019)
- Том: 2523
- Год издания: 2019
- Место издания: CEUR Workshop Proceedings, Germany
- Первая страница: 129
- Последняя страница: 147
- Аннотация: In the areas of Semantic Web and data integration, ontology matching is one of the important steps to resolve semantic heterogeneity. Manual ontology matching is very labor-intensive, time-consuming and prone to errors. So development of automatic or semi-automatic ontology matching methods and tools is quite important. This paper applies machine learning with different similarity measures between ontology elements as features for ontology matching. An approach to combine string-based, language-based and structure-based similarity measures with machine learning techniques is proposed. Logistic Regression, Random Forest classifier and Gradient Boosting are used as machine learning methods. The approach is evaluated on two datasets of Ontology Alignment Evaluation Initiative (OAEI). Copyright © 2019 for this paper by its authors.
- Добавил в систему: Ступников Сергей Александрович