this document, text or image about?' Gradually moving from the left to the right in Figure 3.1, different understandings of this concept evolve

Abstract-We report on two studies in the TREC-2 program that investigated the effect on retrieval performance of combination of multiple representations of TREC topics. In one of the projects, five separate Boolean queries for each of the 50 TREC routing topics and 25 of the TREC ad hoc topics were generated by 75 experienced online searchers. Using the INQUERY retrieval system, these queries were both combined into single queries, and used to produce five separate retrieval results for each topic. In the former case, progressive combination of queries led to progressively improving retrieval performance, significantly better than that of single queries, and at least as good as the best individual single-query formulations. In the latter case, data fusion of the ranked lists also led to performance better than that of any single list. In the second project, two automatically produced vector queries and three versions of a manually produced P-norm extended Boolean query for each routing and ad hoc topic were compared and combined. This project investigated six different methods of combination of queries, and the combination of the same queries on different databases. As in the first project, progressive combination led to progressively improving results, with the best results, on average, being achieved by combination through summing of retrieval status values. Both projects found that the best method of combination often led to results that were better than the best performing single query. The combined results from the two projects have also been combined by data fusion. The results of this procedure show that combining evidence from completely different systems also leads to performance improvement. 1.

The objective of the paper is to amalgamate theories of text retrieval from various research traditions into a cognitive theory for information retrieval interaction. Set in a cognitive framework, the paper outlines the concept of polyrepresentation applied to both the user's cognitive space and the information space of IR systems. The concept seeks to represent the current user's information need, problem state, and domain work task or interest in a structure of causality. Further, it implies that we should apply different methods of representation and a variety of IR techniques of different cognitive and functional origin simultaneously to each semantic full-text entity in the information space. The cognitive differences imply that by applying cognitive overlaps of information objects, originating from different interpretations of such objects through time and by type, the degree of uncertainty inherent in IR is decreased. Polyrepresentation and the use of cognitive overlaps are associated with, but not identical to, data

According to the logical model of Information Retrieval (IR), the task of IR can be described as the extraction, from a given document base, of those documents d that, given a query q, make the formula d → q valid, where d and q are formulae of the chosen logic and “→ ” denotes the brand of logical implication formalized by the logic in question. In this paper, although essentially subscribing to this view, we propose that the logic to be chosen for this endeavour be a Terminological Logic (TL): accordingly, the IR task becomes that of singling out those documents d such that d � q, where d and q are terms of the chosen TL and “�” denotes subsumption between terms. We call this the terminological model of IR. TLs are particularly suitable for modelling IR; in fact, they can be employed: 1) in representing documents under a variety of aspects (e.g. structural, layout, semantic content); 2) in representing queries; 3) in representing lexical, “thesaural ” knowledge. The fact that a single logical language can be used for all these representational endeavours ensures that all these sources of knowledge will participate in the retrieval process in a uniform and principled way. In this paper we introduce Mirtl, a TL for modelling IR according to the above guidelines; its syntax, formal semantics and inferential algorithm are described. 1

‘Probability is expectation founded upon partial knowledge.’

This article proposes a framework, a so called information field, which allows information retrieval mechanisms to be compared inductively instead of experimentally. Such a comparison occurs as follows: Both retrieval mechanisms are first mapped to an associated information field. Within the field, the axioms that drive the retrieval process can be filtered out. In this way, the implicit assumptions governing an information retrieval mechanism can be brought to light. The retrieval mechanisms can then be compared according to which axioms they are governed by. Using this method it is shown that Boolean retrieval is more powerful than a strict form of coordinate retrieval. The salient point is not this result in itself, but how the result was achieved. 1 Introduction The logic based approach to information retrieval has been around for some time now. So far, a number of inference mechanisms, both strict and plausible, have been proposed for driving the retrieval process [15, 6, 4, 12]....

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Abstract. In this paper we report on the application of two contrasting types of relevance feedback for web retrieval. We compare two systems; one using explicit relevance feedback (where searchers explicitly have to mark documents relevant) and one using implicit relevance feedback (where the system endeavours to estimate relevance by mining the searcher's interaction). The feedback is used to update the display according to the user’s interaction. Our research focuses on the degree to which implicit evidence of document relevance can be substituted for explicit evidence. We examine the two variations in terms of both user opinion and search effectiveness. 1

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

Query formulation in the context of large conceptual schemata is known to be a hard problem. When formulating ad-hoc queries users may become overwhelmed by the vast amount of information that is stored in the information system; leading to a feeling of lost in conceptual space. In this article we develop a strategy to cope with this problem. This strategy is based on ideas from the information retrieval world. In particular the query by navigation mechanism and the stratified hypermedia architecture. The stratified hypermedia architecture is used to describe the information contained in the information system on multiple levels of abstraction. When using our approach to the formulation of queries, a user will first formulate a number of simple queries corresponding to linear paths through the information structure. The formulation of the linear paths is the result of the explorative phase of query formulation. Once users have specified a number of these linear paths, they ma...