Machine Learning for the Semantic Web: Lessons Learnt and Next Research Directions

Tracking #: 2191-3404

This paper is currently under review
Authors: 
Claudia d'Amato

Responsible editor: 
Guest Editor 10-years SWJ

Submission type: 
Other
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 an 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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Under Review