Chantal Soulé-Dupuy

Query modification based on relevance back-propagation in an ad hoc environment

It is well-known that relevance feedback is a method significant in improving the effectiveness of information retrieval systems. Improving effectiveness is important since these information retrieval systems must gain access to large document collections distributed over different distant sites. As a consequence, efforts to retrieve relevant documents have become significantly greater. Relevance feedback can be viewed as an aid to the information retrieval task. In this paper, a relevance feedback strategy is presented. The strategy is based on back-propagation of the relevance of retrieved documents using an algorithm developed in a neural approach. This paper describes a neural information retrieval model and emphasizes the results obtained with the associated relevance back-propagation algorithm in three different environments: manual ad hoc, automatic ad hoc and mixed ad hoc strategy (automatic plus manual ad hoc).

Querying a hypertext information retrieval system by the use of classification

Abstract We present in this paper a navigation approach using a combination of functionalities encountered in classification processes, Hypertext Systems and Information Retrieval Systems. Its originality lies in the cooperation of these mechanisms to restrict the consultation universe, to locate faster the searched information, and to tackle the problem of disorientation when consulting the restricted Hypergraph of retrieved information. A first version of the SYRIUS system has been developed integrating both Hypertext and Information Retrieval functionalities that we have called Hypertext Information Retrieval System (H.I.R.S.). This version has been extended using classification mechanisms. The graphic interface of this new system version is presented here. Querying the system is done through common visual representation of the database Hypergraph. The visualization of the Hypergraph can be parameterized focusing on several levels (classes, links,...).

Connectionist and Genetic Approaches for Information Retrieval

In the past few decades, knowledge based techniques have made an impressive contribution to intelligent information retrieval (IR). These techniques stem from research on artificial intelligence, neural networks (NN) and genetic algorithms (GA) and are used to answer three main IR tasks: information modelling, query evaluation and relevance feedback. The paper describes IR approaches based on connectionist and genetic approaches. Our goal is to take benefits of these techniques to fulfill the user information needs. More precisely a multi-layer NN, Mercure, is used to represent the document space in an associative way, to evaluate the query using spreading activation and to implement a relevance feedback process by relevance back-propagation. Another query reformulation technique is investigated which uses the GA approach. The GA generates several queries that explore different areas of the document space. Experiments and results obtained with both techniques are shown and discussed.

Collaborative and Social Information Retrieval and Access: Techniques for Improved User Modeling

Professionals are continually presented with numerous information sources creating the need to determine their relevance within the huge amount of available information. Collaborative and Social Information Retrieval and Access: Techniques for Improved User Modeling presents current state-of-the-art developments including case studies, challenges, and trends. Covering topics such as recommender systems, user profiles, and collaborative filtering, this book informs and educates academicians, researchers, and field practitioners on the latest advancements in information retrieval.

Personalized information access through flexible and interoperable profiles

When searching information, any user has to face huge cognitive efforts to obtain accurate and relevant results. The search task includes a set of complementary sub-tasks in which the user needs to be necessarily involved. But, the real place of the users is not obvious without an effective knowledge of their context, environment, and so on. So we assume that a better knowledge of the user and of available information should make it possible to implement techniques aimed at adapting the retrieved information contents, as well as the search process itself. This personalization mainly relies on the definition of profiles. Since applications principally manage specific user/information profiles (structure and content), we propose in this paper a generic and a flexible profile model. This latter facilitates the construction and the interoperability of various profiles coming from different applications and/or having different structure/content. This paper presents the way the different resources (user, information...) can be modeled within the information search process and its related tasks. Then, we discuss the usefulness of profiles in such processes/tasks. Finally we present the generic and the flexible profile model we propose.

Formal modeling of multistructured documents

The quantity of digital documents available is still growing. The various contexts of use of such documents need several kinds of descriptions of their contents and structures. Thus a same document can be described according to several concurrent structures. Designing models and tools to exploit these various kinds of structures simultaneously presents a real challenge. In this way we have built document repositories to achieve this aim. Indeed, we proposed fragmentation techniques to manage the various issues related to the management of multistructured documents (representation, storage, reconstruction, and management of concurrent structures). This paper is dedicated to the presentation of the formal model. We propose to describe with precision and concision the various concepts related to the multi-structured documents as well as rules related to the organization of these documents.

A Connexionist Model for Information Retrieval

This paper describes a connectionist architecture for an information retrieval system based on neural networks. This approach allows to define a “dynamic” thesaurus, in order to improve the construction of a documentary base and to perform associative information retrieval. We suggest a set of rules to activate cells in order to start an activation/propagation process on which the associative information retrieval is based. A learning mechanism is also started. These two notions allow to develop automatic reformulations of queries and dynamic restructuration of the information base.

Diamond multidimensional model and aggregation operators for document OLAP

On-Line Analytical Processing (OLAP) has generated methodologies for the analysis of structured data. However, they are not appropriate to handle document content analysis. Because of the fast growing of this type of data, there is a need for new approaches abling to manage textual content of data. Generally, these data exist in XML format. In this context, we propose an approach of construction of our Diamond multidimensional model, which includes semantic dimension to better consider the semantics of textual data In addition, we propose new aggregation operators for textual data in OLAP environment.

Improving Information Retieval by Modelling Business Context

Retrieving information is a central operation when accomplishing most of today business tasks. Unfortunately, a person has hard time finding relevant information necessary to accomplish its business task due to the complexity of these information processes. In certain corporate bodies (aeronautical, automotive, etc.) these tasks are critical and should be run from specific information. As a solution, information retrieval systems must take into account the user context which integrates the user business task to improve its overall accuracy and facilitate the achievement of the task. To solve this problem we present in this paper the architecture of an information retrieval system applicable to a business situation. It is based on a triptych context model which integrates three dimensions: user, task and environment. This model is associated with a learning process and to a process that creates according to the conjunction of all the contextual factors, a particular situation. This situation will be exploited by the information search system to improve its accuracy in being the entry point of the information retrieval system. We explain how this system can improve the information search in a business context by taking into account cognitive, technical and social aspects.

Modeling and Exploitation of Multistructured Documents

More than half of information used in organizations is stored in documents. The diversity of their uses implies several descriptions and thus several decomposition needs of these documents (and so several structures). Hence, designing models and tools to exploit simultaneously these various kinds of structures presents a real challenge. To fulfill these needs, we propose a modeling approach based on fragmentation techniques to manage the various issues related to the management of multistructured documents (representation, storage, reconstruction, and management of concurrent structures). Three exploitation modes are proposed according to the different kinds of information searched: information, data interrogation and multidimensional analysis.