Machine Learning methods have been introduced in the Semantic Web for solving problems such as link and type prediction, ontology enrichment and completion (both at terminological and assertional level). Whilst initially mainly focussing on symbol-based solutions, recently numeric-based approaches have received major attention, motivated by the need to scale on the very large Web of Data. In this paper, the most representative proposals, belonging to the aforementioned categories are surveyed, jointly with the analysis of their main peculiarities and drawbacks. Afterwards the main envisioned research directions for further developing Machine Learning solutions for the Semantic Web are presented.

Machine Learning for the Semantic Web: Lessons learnt and next research directions

Claudia d'Amato
Membro del Collaboration Group
2020

Abstract

Machine Learning methods have been introduced in the Semantic Web for solving problems such as link and type prediction, ontology enrichment and completion (both at terminological and assertional level). Whilst initially mainly focussing on symbol-based solutions, recently numeric-based approaches have received major attention, motivated by the need to scale on the very large Web of Data. In this paper, the most representative proposals, belonging to the aforementioned categories are surveyed, jointly with the analysis of their main peculiarities and drawbacks. Afterwards the main envisioned research directions for further developing Machine Learning solutions for the Semantic Web are presented.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11586/286609
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