An information retrieval model based on probabilistic network

This paper presents an information retrieval model based on a kind of probabilistic network. The dependence relationships between terms and documents are represented by the topology of the network. The network contains two parts., the term layer and the document layer. The term relationships are represented as an undirected probabilistic net-work, with the directed arcs toward the nodes indexed in the document layer. Using a learning algorithm, the topology and the probabilities encoding the strength of the relationships can be learned from the document collection automatically. We also provide a two-part inference process to obtain the relevance of the documents to a given query.