Document - SELEcTor: Discovering Similar Entities on LinkEd DaTa by Ranking Their Features

2017

Conference article Restricted

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

Ruback L., Casanova M. A., Renso C., Lucchese C.

Similarity Linked Open Data Ranked list

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.

Source: ICSC 2017 - IEEE 11th International Conference on Semantic Computing, pp. 117–124, San Diego, CA, USA, 30 January-2 February 2017

Publisher: IEEE, New York, USA

Metrics

Back to previous page

Cite as

BibTeX entry

@inproceedings{oai:it.cnr:prodotti:384698,
	title = {SELEcTor: Discovering Similar Entities on LinkEd DaTa by Ranking Their Features},
	author = {Ruback L. and Casanova M. A. and Renso C. and Lucchese C.},
	publisher = {IEEE, New York, USA},
	doi = {10.1109/icsc.2017.46},
	booktitle = {ICSC 2017 - IEEE 11th International Conference on Semantic Computing, pp. 117–124, San Diego, CA, USA, 30 January-2 February 2017},
	year = {2017}
}