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

This paper follows a formal approach to information retrieval based on statistical language models. By introducing some simple reformulations of the basic language modeling approach we introduce the notion of importance of a query term. The importance of a query term is an unknown parameter that explicitly models which of the query terms are generated from the relevant documents (the important terms), and which are not (the unimportant terms). The new language modeling approach is shown to explain a number of practical facts of today’s information retrieval systems that are not very well explained by the current state of information retrieval theory, including stop words, mandatory terms, coordination level ranking and retrieval using phrases.

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 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...

There is a need to develop more intelligent means for handling text in applications such as information retrieval, information filtering, and message classification. This raises the need for mechanisms for ascertaining what an item of text is about. Even though natural language processing offers the best results, it is not always viable. A less accurate, but more viable alternative, is to reason with keywords in the text. Unfortunately, classical reasoning is often inadequate for determining from some keywords what a text is about. In particular, it does not allow context-dependent interpretation of keywords. So for example, if some text has the keyword oil, it is usually also about minerals, though with exceptions such as when it has the keyword cooking. To address this kind of problem, we consider a model of "aboutness" based on default logic. 1 Introduction There is a need to develop more sophisticated, more intelligent, means for handling textual information in computing. Take fo...

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this article we present a framework based on Situation Theory which models the Query by Navigation process in a Hypertext environment. Descriptors in the Hyperindex are transformed to so-called infons. Documents in the Hyperbase are represented as situations, which can support infons. Similarly, search paths are modeled as a number of followed information links between the infons expressing the information need of a searcher. Based on the user's search path two preference relations are proposed. Firstly, a preference relation for documents, which is expressing a kind of relevance ranking. This ranking is based on a

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 article can be seen as integrating nonmonotonic reasoning and information retrieval. Searching is realized via navigating through an information space called a hyperindex. The user preferences suggested by the path are represented as defaults. and/or preclusion relationships. The semantics of navigation paths are defined in the style of model preference logic. Some information retrieval related properties of these semantics are given. Sound inference rules corresponding to these semantics are also provided. These rules may be used to infer descriptors which are consistent with the preferences for information inherent in the user's navigation path. Such inferences can be used for query expansion or to dynamically alter the information space through which the user is browsing. 1 Introduction The information retrieval (IR) problem can be described as the quest to find the set of relevant information objects corresponding to a given information need, which is represented by a reque...

The notion of aboutness is fundamental to information retrieval. Assume there is a document d which is about query q. Now, if information is added to d yielding ~ d, the question arises whether document ~ d is about q? In other words, is aboutness monotonic with respect to information composition? This article shows that aboutness does have nonmonotonic character with respect to composition.