The use of inference networks to support document retrieval is introduced. A network-basead retrieval model is described and compared to conventional probabilis-tic and Boolean models. 1

The use of inference networks to support document retrieval is introduced. A network-based retrieval model is described and compared to conventional probabilistic and Boolean models. The performance of a retrieval system based on the inference network model is evaluated and compared to performance with conventional retrieval models,

In this paper, an introduction and survey over probabilistic information retrieval (IR) is given. First, the basic concepts of this approach are described: the probability ranking principle shows that optimum retrieval quality can be achieved under certain assumptions; a conceptual model for IR along with the corresponding event space clarify the interpretation of the probabilistic parameters involved. For the estimation of these parameters, three different learning strategies are distinguished, namely query-related, document-related and description-related learning. As a representative for each of these strategies, a specific model is described. A new approach regards IR as uncertain inference; here, imaging is used as a new technique for estimating the probabilistic parameters, and probabilistic inference networks support more complex forms of inference. Finally, the more general problems of parameter estimation, query expansion and the development of models for advanced document representations are discussed.

The classical Probability Ranking Principle (PRP) forms the theoretical basis for probabilistic Information Retrieval (IR) models, which are dominating IR theory since about 20 years. However, the assumptions underlying the PRP often do not hold, and its view is too narrow for interactive information retrieval (IIR). In this paper, a new theoretical framework for interactive retrieval is proposed: The basic idea is that during IIR, a user moves between situations. In each situation, the system presents to the user a list of choices, about which s/he has to decide, and the first positive decision moves the user to a new situation. Each choice is associated with a number of cost and probability parameters. Based on these parameters, an optimum ordering of the choices can the derived- the PRP for IIR. The relationship of this rule to the classical PRP is described, and issues of further research are pointed out. 1

Stage 1 calculates the relevance between the given user information need (query) and each of the documents Producing a “best guess ” at the relevance, e.g. the BM25 and the language modelling approaches Stage 2 presents (normally rank) relevant documents The probability ranking principle (PRP) states that “the system should rank documents in order of decreasing probability of relevance ” [Cooper(1971)] Under certain assumptions, the overall effectiveness, e.g., expected Precision, is maximized