Name
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 stateof-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICSC.2017.46 | 2017 IEEE 11th International Conference on Semantic Computing (ICSC) |
Keywords | Field | DocType |
Linked Data,Entity Similarity,Rank correlation | Data mining,Ranking,Information retrieval,Computer science,Linked data,Exploit | Conference |
ISSN | ISBN | Citations |
2325-6516 | 978-1-5090-4285-2 | 3 |
PageRank | References | Authors |
0.42 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lívia Ruback | 1 | 10 | 1.93 |
| Marco Antonio Casanova | 2 | 178 | 24.18 |
| Chiara Renso | 3 | 925 | 76.04 |
| Claudio Lucchese | 4 | 1104 | 73.76 |