SELEcTor: Discovering Similar Entities on LinkEd DaTa by Ranking their Features

Dec. 10 2016

Accepted at ICSC ’17: IEEE International Conference on Semantic Computing [1].

Abstract. Several approaches have been used in the last years to compute similarity between entities. In this paper, we present a novel approach to compute similarity between entities using their features available as Linked Data. The key idea of the proposed framework, called SELEcTor, is to exploit ranked lists of features extracted from Linked Data sources as a representation of the entities we want to compare. The similarity between two entities is thus mapped to the problem of comparing two ranked lists. Our experiments, conducted with museum data from DBpedia, demonstrate that SELEcTor achieves better accuracy than state- of-the-art methods.


[1]   Livia Ruback, Marco Antonio Casanova, Claudio Lucchese, and Chiara Renso. SELEcTor: Discovering similar entities on linked data by ranking their features. In ICSC ’17: IEEE International Conference on Semantic Computing, 2017.

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