Foundations of Intelligent Systems

Volume 5722 of the series Lecture Notes in Computer Science pp 241-250

Boosting a Semantic Search Engine by Named Entities

  • Annalina CaputoAffiliated withDepartment of Computer Science, University of Bari
  • , Pierpaolo BasileAffiliated withDepartment of Computer Science, University of Bari
  • , Giovanni SemeraroAffiliated withDepartment of Computer Science, University of Bari

* Final gross prices may vary according to local VAT.

Get Access


Traditional Information Retrieval (IR) systems are based on bag-of-words representation. This approach retrieves relevant documents by lexical matching between query and document terms. Due to synonymy and polysemy, lexical methods produce imprecise or incomplete results. In this paper we present SENSE (SEmantic N-levels Search Engine), an IR system that tries to overcome the limitations of the ranked keyword approach, by introducing semantic levels which integrate (and not simply replace) the lexical level represented by keywords. Semantic levels provide information about word meanings, as described in a reference dictionary, and named entities. This paper focuses on the named entity level. Our aim is to prove that named entities are useful to improve retrieval performance. We exploit a model able to capture entity relationships, although they are not explicit in documents text. Experiments on CLEF dataset prove the effectiveness of our hypothesis.