This article surveys probabilistic approaches to modeling information retrieval. The basic concepts of probabilistic approaches to information retrieval are outlined and the principles and assumptions upon which the approaches are based are presented. The various models proposed in the development of IR are described, classified, and compared using a common formalism. New approaches that constitute the basis of future research are described

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What is that makes a “good ” logical model of IR? What are the guidelines that we should follow when we want to build one, and how much can we depart from these guidelines and still claim to have a logical model of IR? We have been motivated to write this note from our dissatisfaction with the fact that there seem to be many competing, incompatible views of what a logical model of IR should consist of; we think some of these views are misleading. 1 Information Retrieval and modelling In recent years, researchers in Information Retrieval (IR) have devoted an increasing amount of work to the design of models of IR, i.e. of theoretical descriptions of the IR task that could serve both as specifications for building running systems, and as theoretical tools for abstractly investigating the relative effectiveness of systems built along their guidelines. Modelling is fundamentally an activity of abstraction. A model is a description of a system that concentrates on the most important, architectural features of the system, and leaves out details that are believed not to be

The use of logic to model the information retrieval process has become an established research area. Nevertheless, many people in the information retrieval community do not yet appreciate the work performed in this area, mainly because they do not understand logical formalisms, and hence cannot see the connection between logic and information retrieval. This paper aims at resolving the problem. It introduces the formalisms used in logical models for information retrieval, shows the use of logic to build the models, and presents a brief overview of some of the current logical models in information retrieval. 2 1 INTRODUCTION It has been argued that current information retrieval (IR) models offer only simplistic and specific representations of information (Chiaramella and Chevallet, 1992, Nie, 1990, van Rijsbergen, 1989). There is, therefore, a need for the development of a new formalism able to model IR systems in a more generic manner, hence capturing information as it appear...

This paper addresses the notion of aboutness in information retrieval. First, an exposition is given on how aboutness relates to relevance - a fundamental notion in information retrieval. A short summary is given on how aboutness is defined in more prominent information retrieval models. A model-theoretic definition of aboutness is then analyzed in an abstract setting using so called information fields. These allows properties of aboutness to be expressed independent of any given information retrieval model. As a consequence, information retrieval models can be theoretically compared according to what aboutness postulates they support. The Boolean and Coordinate retrieval models are compared in this fashion. In addition to model-theoretic aboutness, preferential entailment and conditional probabilities are employed to define aboutness between primitive information carriers. The preferential entailment approach is based on a preference semantics derived from nonmonotonic logics. The non...

The evaluation of an implication by Imaging is a logical technique developed in the framework of Conditional Logics. In 1993 a logical model of IR called "Retrieval by Logical Imaging" was proposed by some of the authors of this paper and tested using some classical IR test collections. In this paper we report on the challenges posed by trying to apply such a model to a large test collection of the size of TREC-B. The problems we found and the way we put together ideas and efforts to solve them are indicative of the troubles one might find in trying to implement and experiment with a "complex" logical model of IR. We believe our efforts could set an example for other researchers working on logical models of IR to try to implement their models in such a way that they can cope with the size of real life collections, though preserving the formal "beauty" of their logical models. Address to which correspondence should be sent: Ian Ruthven, Department of Computing Science, University of ...

Abstract. In classic Information Retrieval systems a relevant document will not be retrieved in response to a query if the document and query representations do not share at least one term. This problem, known as “term mismatch”, has been recognised for a long time by the Information Retrieval community and a number of possible solutions have been proposed. Here I present a preliminary investigation into a new class of retrieval models that attempt to solve the term mismatch problem by exploiting complete or partial knowledge of term similarity in the term space. The use of term similarity enables to enhance classic retrieval models by taking into account non-matching terms. The theoretical advantages and drawbacks of these models are presented and compared with other models tackling the same problem. A preliminary experimental investigation into the performance gain achieved by exploiting term similarity with the proposed models is presented and discussed.

this paper is: Independent of any given IR model, and examined within an information -based, abstract framework, what are commonsense properties of aboutness (and its dual, non-aboutness)? We propose a set of properties characterizing aboutness and non-aboutness from a commonsense perspective. Special attention is paid to the rules prescribing conservative behaviour of aboutness with respect to information composition. The interaction between aboutness and non-aboutness is modeled via normative rules. The completeness, soundness and consistency of the aboutness proof systems are analyzed and discussed. A case study based on monotonicity shows that many current IR systems are either monotonic or non-monotonic. An interesting class of IR models, namely those that are conservatively monotonic, is identified. 1 . Introduction You are sitting in a bus and two people in front of you are talking. The first says to the second, "I went to see so-andso film last night", to which

This paper claims that Belief Revision can be seen as a theoretical framework for document ranking in Extended Boolean Models. For a model of Information Retrieval based on propositional logic, we propose a similarity measure which is equivalent to a P-Norm case. Therefore it shares the PNorm good properties and behaviour. Besides, it is theoretically ensured that this measure follows the notion of proximity between the documents and the query. The logical model can naturally deal with incomplete descriptions of documents and the similarity values are also obtained for this case. 1 Introduction Logical approaches have been proposed to model Information Retrieval (IR) in a formal framework. Van Rijsbergen was the pioneer in thinking that logic could help in the retrieval of relevant documents [21]. Moreover, he proposed logic as a new theoretical framework for investigating IR. Given d, a logical representation of a document, and q, a logical representation of a query, retrieval is si...

this article, we propose to use inductive evaluation for functional benchmarking of IR models as a complement of the traditional experiment-based performance benchmarking. We define a functional benchmark suite in two stages: the evaluation criteria based on the notion of "aboutness," and the formal evaluation methodology using the criteria. The proposed benchmark has been successfully applied to evaluate various well-known classical and logic-based IR models. The functional benchmarking results allow us to compare and analyze the functionality of the different IR models