Integrating notion of agency and semantics in information retrieval: an intelligent multi-agent model

We present an intelligent information retrieval model based on multi-agent paradigm and conceptual graphs. The growing amount of on-line information and its dynamic nature forces us to reconsider existing passive approaches for information retrieval. Because of this ever-growing size of information sources the burden of retrieving information can not be simply left on users. We attempt to handle this problem through software agents. Our model makes use of a user modeling agent (UMA), a facilitator and integrator (FACINT) module and a number of retrieval agents. UMA is responsible for creating a user profile. Actual retrieval is done by retrieval agents. FACINT is responsible for controlling and coordinating activities of various agents. It also does final ranking of the documents based on conceptual graph (CG) representation of documents. The use of CG brings semantics in making relevance judgment resulting in improved ranking. The model proposed by us is simple, efficient, scalable and can work actively as well as passively.