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

The Internet has led to an increase in the quantity and diversity of information available for searching. Furthermore, users

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...

This paper is an essay to convince the reader that Situation Theory presents many characteristics that are both adequate and appropriate for the study of IR. Here we concentrate on the ones we have experience with:

In this paper we review ways of representing incoherent 'knowledge' in a consistent way, where the use of modal logic and Kripke-style semantics is put central. Starting with a presentation of the basic modal framework, we discuss the basic modal systems K, KD (with an excursion to the representation of conflicting norms in deontic logic) and Chellas' minimal modal logic D. Next we look at the epistemic logics KD45, S4 and S5, including the logical omniscience problem and several non-standard modal logics to overcome this problem. After this we turn to the issue of reasoning by default, where a conflict of defaults (or default beliefs) may arise. We give an epistemic treatment of default reasoning, and treat the way conflicts of defaults can be solved viewed from the more general perspective of resolving conflicts in meta- level reasoning. Furthermore, special attention is paid to specificity in default reasoning as a principle to solve these conflicts, for which we develop an extension of Halpern & Moses' theory of honest formulas. Finally, we discuss several numerical modal logics in their capacity of ways of representation of incoherent information.

Resource Discovery is the term commonly used to refer to the exercise of locating, accessing, retrieving, and managing relevant resources (eg information and services) for a user from widely distributed heterogeneous networks. The Resource Discovery Unit of the Research Data Network Cooperative Research Centre is working on tools and technologies which make these tasks easier in the Open Information Locator (OIL) project. This paper aims to present and discuss some of the central issues in the area of Resource Discovery that the OIL project is addressing. It also aims to position the OIL project with respect to related research fields including Information Retrieval, Distributed Databases, and Digital Libraries. 1 Global Resource Discovery 1.1 Resource Discovery: a definition Resource Discovery is a multidisciplinary domain (as shown in Figure 1). Researchers in each of these disciplines have differing views on what resource discovery is. The resulting polysemic definition of resourc...

(DSTC Tech Report 34) Resource Discovery is the term commonly used to refer to the exercise of locating, accessing, retrieving, and managing relevant resources (eg information and services) for a user from widely distributed heterogeneous networks. The Resource Discovery Unit of the Research Data Network Cooperative Research Centre is working on tools and technologies which make these tasks easier in the Open Information Locator (OIL) project. This paper aims to present and discuss some of the central issues in the area of Resource Discovery that the OIL project is addressing. It also aims to position the OIL project with respect to related research fields including Information Retrieval, Distributed Databases, and Digital Libraries. 1 Global Resource Discovery 1.1 Resource Discovery: a definition Resource Discovery is a multidisciplinary domain (as shown in Figure 1). Researchers in each of these disciplines have differing views on what resource discovery is. The resulting polysemi...

Information retrieval is the quest to find those information objects relevant to a given information need. Relevance is a difficult notion to define operationally. As a consequence information retrieval mechanisms are typically driven by the decision of when one information carrier (e.g. a document) is about another (e.g. a query). As documents and queries are typically complex representations built up from so called terms, the aboutness decision relies on determining whether one term is about another. This article addresses the question of term aboutness within a framework provided by so called Information Containment Belief Networks. The latter are a computationally efficient subclass of belief network motivated from an underlying algebraic theory of information. Various properties of Information Containment Belief Networks are presented, with particular emphasis on those properties dealing with their computational efficiency. Some probabilistic strategies for computing term aboutnes...

van der Weide ©Copyright in this paper belongs to the author(s) Published in collaboration with the

This article describes and analyses the retrieval component of the Profile Information Filtering Project of the University of Nijmegen. The overall structure of this project, serving as the context for the retrieval component, is stated. This component is called the Retrieval Engine and will be implemented as an intelligent retrieval agent, using sophisticated techniques from artificial intelligence. A synthesis between information retrieval and information filtering has to be found, coping with challenging problems stemming from the combination of the difficulties of both fields. The Retrieval Engine should be capable of giving an explanation of why a document was found relevant to the information need of the user. The techniques used will rely on sophisticated natural language processing. The techniques to establish relevance degrees for documents will consist of two parts: a symbolic and a numeric one. This allows for a mechanism that is both explainable and exact. Interesting appro...