Computing Entity Semantic Similarity using Ranked Lists of Features

Tracking #: 1737-2949

This paper is currently under review
Authors: 
Livia Ruback
Marco Antonio Casanova
Chiara Renso
Claudio Lucchese
Alexander Mera
Grettel Monteagudo García

Responsible editor: 
Claudia d'Amato

Submission type: 
Full Paper
Abstract: 
This article presents a novel approach to estimate semantic entity similarity using entity features available as Linked Data. The key idea is to exploit ranked lists of features, extracted from Linked Data sources, as a representation of the entities to be compared. The similarity between two entities is then estimated by comparing their ranked lists of features. The article describes experiments with museum data from DBpedia, with datasets from a LOD catalogue, and with computer science conferences from the DBLP repository. The experiments demonstrate that entity similarity, computed using ranked lists of features, achieves better accuracy than state-of-the-art measures.
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