Claude Chrisment

Multiple query evaluation based on an enhanced genetic algorithm

Recent studies suggest that significant improvement in information retrieval performance can be achieved by combining multiple representations of an information need. The paper presents a genetic approach that combines the results from multiple query evaluations. The genetic algorithm aims to optimise the overall relevance estimate by exploring different directions of the document space. We investigate ways to improve the effectiveness of the genetic exploration by combining appropriate techniques and heuristics known in genetic theory or in the IR field. Indeed, the approach uses a niching technique to solve the relevance multimodality problem, a relevance feedback technique to perform genetic transformations on query formulations and evolution heuristics in order to improve the convergence conditions of the genetic process. The effectiveness of the global approach is demonstrated by comparing the retrieval results obtained by both genetic multiple query evaluation and classical single query evaluation performed on a subset of TREC-4 using the Mercure IRS. Moreover, experimental results show the positive effect of the various techniques integrated to our genetic algorithm model.

On using genetic algorithms for multimodal relevance optimization in information retrieval

This article presents a genetic relevance optimization process performed in an information retrieval system. The process uses genetic techniques for solving multimodal problems (niching) and query reformulation techniques commonly used in information retrieval. The niching technique allows the process to reach different relevance regions of the document space. Query reformulation techniques represent domain knowledge integrated in the genetic operators structure to improve the convergence conditions of the algorithm. Experimental analysis performed using a TREC subcollection validates our approach.

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).

Genetic Approach to Query Space Exploration

This paper describes a genetic algorithm approach for intelligent information retrieval. The goal is to find an optimal set of documents which best matches the users needs by exploring and exploiting the document space. More precisely, we define a specific genetic algorithm for information retrieval based on knowledge based operators and guided by a heuristic for relevance multi-modality problem solving. Experiments with TREC-6 French data and queries show the effectiveness of our approach.

Fusing different information retrieval systems according to query-topics: a study based on correlation in information retrieval systems and TREC topics

To evaluate Information Retrieval Systems on their effectiveness, evaluation programs such as TREC offer a rigorous methodology as well as benchmark collections. Whatever the evaluation collection used, effectiveness is generally considered globally, averaging the results over a set of information needs. As a result, the variability of system performance is hidden as the similarities and differences from one system to another are averaged. Moreover, the topics on which a given system succeeds or fails are left unknown. In this paper we propose an approach based on data analysis methods (correspondence analysis and clustering) to discover correlations between systems and to find trends in topic/system correlations. We show that it is possible to cluster topics and systems according to system performance on these topics, some system clusters being better on some topics. Finally, we propose a new method to consider complementary systems as based on their performances which can be applied for example in the case of repeated queries. We consider the system profile based on the similarity of the set of TREC topics on which systems achieve similar levels of performance. We show that this method is effective when using the TREC ad hoc collection.

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,...).

Tie-breaking bias: effect of an uncontrolled parameter on information retrieval evaluation

We consider Information Retrieval evaluation, especially at TREC with the trec_eval program. It appears that systems obtain scores regarding not only the relevance of retrieved documents, but also according to document names in case of ties (i.e., when they are retrieved with the same score). We consider this tie-breaking strategy as an uncontrolled parameter influencing measure scores, and argue the case for fairer tie-breaking strategies. A study of 22 TREC editions reveals significant differences between the Conventional unfair TRECs strategy and the fairer strategies we propose. This experimental result advocates using these fairer strategies when conducting evaluations.

Organization of digital resources as an original facet for exploring the quiescent information capital of a community

Knowledge workers organize the documents they need for daily task achievement in their personal information spaces (PISs). For a community, people’s PISs constitute in-house value-added resources. Paradoxically, this information source is poorly exploited, as people tend to use external sources (e.g., the Web), although this is probably poorly appropriate in corporate context. This article tackles such information access issues in the common context. Our contribution consists in a faceted visual interface to explore various facets (points of view) of the information of a community, which remains quiescent otherwise. Besides common facets only based on information contents, we propose a new facet relying on the way users in a community manage and organize information. As a result, our approach exploits knowledge workers’ efforts devoted to PIS management, turning them to profit for all, by fostering mutual benefit between stakeholders. The proposed facet relies on an original organization-based similarity measure that we define and experiment.