Abstract. Research on multimedia information retrieval (MIR) has recently witnessed a booming interest. A prominent feature of this research trend is its simultaneous but independent materialization within several fields of computer science. The resulting richness of paradigms, methods and systems may, on the long run, result in a fragmentation of efforts and slow down progress. The primary goal of this study is to promote an integration of methods and techniques for MIR by contributing a conceptual model that encompasses in a unified and coherent perspective the many efforts that are being produced under the label of MIR. The model offers a retrieval capability that spans two media, text and images, but also several dimensions: form, content and structure. In this way, it reconciles similarity-based methods with semantics-based ones, providing the guidelines for the design of systems that are able to provide a generalized multimedia retrieval service, in which the existing forms of retrieval not only coexist, but can be combined in any desired manner. The model is formulated in terms of a fuzzy description logic, which plays a twofold role: (1) it directly models semantics-based retrieval, and (2) it offers an ideal framework for the integration of the multimedia and multidimensional aspects of retrieval mentioned above. The model also accounts for relevance feedback in both text and image retrieval, integrating known techniques for taking into account user judgments. The implementation of

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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A Terminological Logic is presented as an information retrieval model, with a four-valued semantics that gives to its inference relation the flavour of relevance, that is a strict connection in meaning between the premises and the conclusion of the arguments licensed by the logic. The logic also permits the expression of meta-knowledge enforcing a closed-world reading of the knowledge concerning specified individuals and primitive concepts. A Gentzen-style, sound and complete calculus for reasoning in the logic is given, thus establishing the basis for an information retrieval engine.

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...

Ontologies play a crucial role in the development of the Semantic Web as a means for defining shared terms in web resources. They are formulated in web ontology languages, which are based on expressive description logics. Significant research efforts in the semantic web community are recently directed towards representing and reasoning with uncertainty and vagueness in ontologies for the Semantic Web. In this paper, we give an overview of approaches in this context to managing probabilistic uncertainty, possibilistic uncertainty, and vagueness in expressive description logics for the Semantic Web.

This paper constitutes a proposal for an efficient and effective logical information retrieval system. Following a relational indexing approach, which is in our opinion a necessity to cope with the emerging applications such as those based on multimedia, we use the conceptual graphs formalism as our indexing language. This choice allows for relational indexing support and captures all the useful properties of the logical information retrieval model, in a workable system. First order logic and standard information retrieval techniques are combined together, to the same effect: obtaining an expressive system, able to accurately handle complex documents, improve retrieval effectiveness, and achieve good time performance. Experimentations on an image test collection, within a system available on the Web, provide an illustration of the role that logic may have in the future development of information retrieval systems. 1 Introduction The emergence of new applications, such as those based ...

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...

Semantic Portal is the next generation of web portals that are powered by Semantic Web technologies for improved information sharing and exchange for a community of users. Current methods of searching in Semantic Portals are limited to keyword-based search using information retrieval (IR) techniques, ontology-based formal query and reasoning, or a simple combination of the two. In this paper, we propose an enhanced model that tightly integrates IR with formal query and reasoning to fully utilize both textual and semantic information for searching in Semantic Portals. The model extends the search capabilities of existing methods and can answer more complex search requests. The ideas in a fuzzy description logic (DL) IR model and a formal DL query method are employed and combined in our model. Based on the model, a semantic search service is implemented and evaluated. The evaluation shows very large improvements over existing methods.