Many current recommender systems exploit textual annotations (tags) provided by users to retrieve and suggest online contents. The text-based recommendation provided by these systems could be enhanced (i) using unambiguous identifiers representative of tags and (ii) exploiting semantic relations among tags which are impossible to be discovered by traditional textual analysis. In this paper we concentrate on annotation and retrieval of web content, exploiting semantic tagging with DBpedia. We use semantic information stored in the DBpedia dataset and propose a new hybrid ranking system to rank keywords and to expand queries formulated by the user. Inputs of our ranking system are (i) the DBpedia dataset; (ii) external information sources such as classical search engine results and social tagging systems. We compare our approach with other RDF similarity measures, proving the validity of our algorithm with an extensive evaluation involving real users. © 2010 Springer-Verlag Berlin Heidelberg.

Semantic tag cloud generation via DBpedia

Ragone Azzurra;Di Noia T.;Di Sciascio E.
2010-01-01

Abstract

Many current recommender systems exploit textual annotations (tags) provided by users to retrieve and suggest online contents. The text-based recommendation provided by these systems could be enhanced (i) using unambiguous identifiers representative of tags and (ii) exploiting semantic relations among tags which are impossible to be discovered by traditional textual analysis. In this paper we concentrate on annotation and retrieval of web content, exploiting semantic tagging with DBpedia. We use semantic information stored in the DBpedia dataset and propose a new hybrid ranking system to rank keywords and to expand queries formulated by the user. Inputs of our ranking system are (i) the DBpedia dataset; (ii) external information sources such as classical search engine results and social tagging systems. We compare our approach with other RDF similarity measures, proving the validity of our algorithm with an extensive evaluation involving real users. © 2010 Springer-Verlag Berlin Heidelberg.
2010
978-3-642-15207-8
978-3-642-15208-5
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/401589
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 13
  • ???jsp.display-item.citation.isi??? 9
social impact