Enhancing Semantic Search using N-Levels Document Representation

The traditional strategy performed by Information Retrieval (IR) systems is ranked keyword search: For a given query, a list of documents, ordered by relevance, is returned. Relevance computation is primarily driven by a basic string-matching operation. To date, several attempts have been made to deviate from the traditional keyword search paradigm, often by introducing some techniques to capture word meanings in documents and queries. The general feeling is that dealing explicitly with only semantic information does not improve significantly the performance of text retrieval systems. This paper presents 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. We show how SENSE is able to manage documents indexed at three separate levels, keywords, word meanings, and entities, as well as to combine keyword search with semantic information provided by the two other indexing levels.