Using Windmill Expansion for Document Retrieval

SEMIOTIKS aims to utilise online information to support the crucial decision–making of those military and civilian agencies involved in the humanitarian removal of landmines in areas of conflict throughout the world. An analysis of the type of information required for such a task has given rise to four main areas of research: information retrieval, document annotation, summarisation and visualisation. The first stage of the research has focused on information retrieval, and a new algorithm, “Windmill Expansion” (WE) has been proposed to do this. The algorithm uses retrieval feedback techniques for automated query expansion in order to improve the effectiveness of information retrieval. WE is based on the extraction of human–generated written phases for automated query expansion. Top and Second Level expansion terms have been generated and their usefulness evaluated. The evaluation has concentrated on measuring the degree of overlap between the retrieved URLs. The less the overlap, the more useful the information provided. The Top Level expansion terms were found to provide 90% of useful URLs, and the Second Level 83% of useful URLs. Although there was a decline of useful URLs from the Top Level to the Second Level, the quantity of relevant information retrieved has increased. The originality of SEMIOTIKS lies in its use of the WE algorithm to help non–domain specific experts automatically explore domain words for relevant and precise information retrieval

QUERY OPTIMISATION USING AN IMPROVED GENETIC ALGORITHM

International audienceThis paper presents an approach to intelligent information retrieval based on genetic heuristics. Recent search has shown that applying genetic models for query optimisation improve the retrieval effectiveness. We investigate ways to improve this process by combining genetic heuristics and information retrieval techniques. More precisely, we propose to integrate relevance feedback techniques to perform the genetic operators and the speciation heuristic to solve the relevance multimodality problem. Experiments, with AP documents and queries issued from TREC, showed the effectiveness of our approach. Keywords: Informatio

Intelligent Fusion of Structural and Citation-Based Evidence for Text Classification

This paper investigates how citation-based information and structural content (e.g., title, abstract) can be combined to improve classification of text documents into predefined categories. We evaluate different measures of similarity, five derived from the citation structure of the collection, and three measures derived from the structural content, and determine how they can be fused to improve classification effectiveness. To discover the best fusion framework, we apply Genetic Programming (GP) techniques. Our empirical experiments using documents from the ACM digital library and the ACM classification scheme show that we can discover similarity functions that work better than any evidence in isolation and whose combined performance through a simple majority voting is comparable to that of Support Vector Machine classifiers

Un Algorithme génétique spécifique à une reformulation multi-requêtes dans un système de recherche d'information

National audienceCet article présente une approche de reformulation de requête fondée sur l'utilisation combinée de la stratégie d'injection de pertinence et des techniques avancées de l'algorithmique génétique. Nous proposons un processus génétique d'optimisation multi-requêtes amélioré par l'intégration des heuristiques de nichage et adaptation des opérateurs génétiques. L'heuristique de nichage assure une recherche d'information coopérative dans différentes directions de l'espace documentaire. L'intégration de la connaissance à la structure des opérateurs permet d'améliorer les conditions de convergence de l'algorithme. Nous montrons, à l'aide d'expérimentations réalisées sur une collection TREC, l'intérêt de notre approche

The Effectiveness of Query-Based Hierarchic Clustering of Documents for Information Retrieval

Hierarchic document clustering has been applied to Information Retrieval (IR) for over three decades. Its introduction to IR was based on the grounds of its potential to improve the effectiveness of IR systems. Central to the issue of improved effectiveness is the Cluster Hypothesis. The hypothesis states that relevant documents tend to be highly similar to each other, and therefore tend to appear in the same clusters. However, previous research has been inconclusive as to whether document clustering does bring improvements. The main motivation for this work has been to investigate methods for the improvement of the effectiveness of document clustering, by challenging some assumptions that implicitly characterise its application. Such assumptions relate to the static manner in which document clustering is typically performed, and include the static application of document clustering prior to querying, and the static calculation of interdocument associations. The type of clustering that is investigated in this thesis is query-based, that is, it incorporates information from the query into the process of generating clusters of documents. Two approaches for incorporating query information into the clustering process are examined: clustering documents which are returned from an IR system in response to a user query (post-retrieval clustering), and clustering documents by using query-sensitive similarity measures. For the first approach, post-retrieval clustering, an analytical investigation into a number of issues that relate to its retrieval effectiveness is presented in this thesis. This is in contrast to most of the research which has employed post-retrieval clustering in the past, where it is mainly viewed as a convenient and efficient means of presenting documents to users. In this thesis, post-retrieval clustering is employed based on its potential to introduce effectiveness improvements compared both to static clustering and best-match IR systems. The motivation for the second approach, the use of query-sensitive measures, stems from the role of interdocument similarities for the validity of the cluster hypothesis. In this thesis, an axiomatic view of the hypothesis is proposed, by suggesting that documents relevant to the same query (co-relevant documents) display an inherent similarity to each other which is dictated by the query itself. Because of this inherent similarity, the cluster hypothesis should be valid for any document collection. Past research has attributed failure to validate the hypothesis for a document collection to characteristics of the collection. Contrary to this, the view proposed in this thesis suggests that failure of a document set to adhere to the hypothesis is attributed to the assumptions made about interdocument similarity. This thesis argues that the query determines the context and the purpose for which the similarity between documents is judged, and it should therefore be incorporated in the similarity calculations. By taking the query into account when calculating interdocument similarities, co-relevant documents can be "forced" to be more similar to each other. This view challenges the typically static nature of interdocument relationships in IR. Specific formulas for the calculation of query-sensitive similarity are proposed in this thesis. Four hierarchic clustering methods and six document collections are used in the experiments. Three main issues are investigated: the effectiveness of hierarchic post-retrieval clustering which uses static similarity measures, the effectiveness of query-sensitive measures at increasing the similarity of pairs of co-relevant documents, and the effectiveness of hierarchic clustering which uses query-sensitive similarity measures. The results demonstrate the effectiveness improvements that are introduced by the use of both approaches of query-based clustering, compared both to the effectiveness of static clustering and to the effectiveness of best-match IR systems. Query-sensitive similarity measures, in particular, introduce significant improvements over the use of static similarity measures for document clustering, and they also significantly improve the structure of the document space in terms of the similarity of pairs of co-relevant documents. The results provide evidence for the effectiveness of hierarchic query-based clustering of documents, and also challenge findings of previous research which had dismissed the potential of hierarchic document clustering as an effective method for information retrieval

Theories of Informetrics and Scholarly Communication

Scientometrics have become an essential element in the practice and evaluation of science and research, including both the evaluation of individuals and national assessment exercises. Yet, researchers and practitioners in this field have lacked clear theories to guide their work. As early as 1981, then doctoral student Blaise Cronin published "The need for a theory of citing" —a call to arms for the fledgling scientometric community to produce foundational theories upon which the work of the field could be based. More than three decades later, the time has come to reach out the field again and ask how they have responded to this call. This book compiles the foundational theories that guide informetrics and scholarly communication research. It is a much needed compilation by leading scholars in the field that gathers together the theories that guide our understanding of authorship, citing, and impact