An Information Retrieval Model for the Semantic Web

Abstract

Exploring the metadata associated with documents in the Semantic Web is a way to increase the precision of information retrieval systems. Systems have been established so far failed to overcome fully the limitations of search based on keywords. Such systems are built from variations of classic models that represent information by keywords and work upon statistical correlations. This work proposes an information retrieval model to find information items with similar semantic content that a given user’s query. The information items internal representation is based on user interest groups, called "semantic cases". The model also defines a similarity measure for ordering the results based on semantic distance between semantic cases items.