Preference relations based unsupervised rank aggregation for metasearch

Rank aggregation mechanisms have been used in solving problems from various domains such as bioinformatics, natural language processing, information retrieval, etc. Metasearch is one such application where a user gives a query to the metasearch engine, and the metasearch engine forwards the query to multiple individual search engines. Results or rankings returned by these individual search engines are combined using rank aggregation algorithms to produce the final result to be displayed to the user. We identify few aspects that should be kept in mind for designing any rank aggregation algorithms for metasearch. For example, generally equal importance is given to the input rankings while performing the aggregation. However, depending on the indexed set of web pages, features considered for ranking, ranking functions used etc. by the individual search engines, the individual rankings may be of different qualities. So, the aggregation algorithm should give more weight to the better rankings while giving less weight to others. Also, since the aggregation is performed when the user is waiting for response, the operations performed in the algorithm need to be light weight. Moreover, getting supervised data for rank aggregation problem is often difficult. In this paper, we present an unsupervised rank aggregation algorithm that is suitable for metasearch and addresses the aspects mentioned above. We also perform detailed experimental evaluation of the proposed algorithm on four different benchmark datasets having ground truth information. Apart from the unsupervised Kendall-Tau distance measure, several supervised evaluation measures are used for performance comparison. Experimental results demonstrate the efficacy of the proposed algorithm over baseline methods in terms of supervised evaluation metrics. Through these experiments we also show that Kendall-Tau distance metric may not be suitable for evaluating rank aggregation algorithms for metasearch

Exploring the reuse of past search results in information retrieval

Les recherches passées constituent pourtant une source d'information utile pour les nouveaux utilisateurs (nouvelles requêtes). En raison de l'absence de collections ad-hoc de RI, à ce jour il y a un faible intérêt de la communauté RI autour de l'utilisation des recherches passées. En effet, la plupart des collections de RI existantes sont composées de requêtes indépendantes. Ces collections ne sont pas appropriées pour évaluer les approches fondées sur les requêtes passées parce qu'elles ne comportent pas de requêtes similaires ou qu'elles ne fournissent pas de jugements de pertinence. Par conséquent, il n'est pas facile d'évaluer ce type d'approches. En outre, l'élaboration de ces collections est difficile en raison du coût et du temps élevés nécessaires. Une alternative consiste à simuler les collections. Par ailleurs, les documents pertinents de requêtes passées similaires peuvent être utilisées pour répondre à une nouvelle requête. De nombreuses contributions ont été proposées portant sur l'utilisation de techniques probabilistes pour améliorer les résultats de recherche. Des solutions simples à mettre en œuvre pour la réutilisation de résultats de recherches peuvent être proposées au travers d'algorithmes probabilistes. De plus, ce principe peut également bénéficier d'un clustering des recherches antérieures selon leurs similarités. Ainsi, dans cette thèse un cadre pour simuler des collections pour des approches basées sur les résultats de recherche passées est mis en œuvre et évalué. Quatre algorithmes probabilistes pour la réutilisation des résultats de recherches passées sont ensuite proposés et évalués. Enfin, une nouvelle mesure dans un contexte de clustering est proposée.Past searches provide a useful source of information for new users (new queries). Due to the lack of ad-hoc IR collections, to this date there is a weak interest of the IR community on the use of past search results. Indeed, most of the existing IR collections are composed of independent queries. These collections are not appropriate to evaluate approaches rooted in past queries because they do not gather similar queries due to the lack of relevance judgments. Therefore, there is no easy way to evaluate the convenience of these approaches. In addition, elaborating such collections is difficult due to the cost and time needed. Thus a feasible alternative is to simulate such collections. Besides, relevant documents from similar past queries could be used to answer the new query. This principle could benefit from clustering of past searches according to their similarities. Thus, in this thesis a framework to simulate ad-hoc approaches based on past search results is implemented and evaluated. Four randomized algorithms to improve precision are proposed and evaluated, finally a new measure in the clustering context is proposed

Image retrieval using automatic region tagging

The task of tagging, annotating or labelling image content automatically with semantic keywords is a challenging problem. To automatically tag images semantically based on the objects that they contain is essential for image retrieval. In addressing these problems, we explore the techniques developed to combine textual description of images with visual features, automatic region tagging and region-based ontology image retrieval. To evaluate the techniques, we use three corpora comprising: Lonely Planet travel guide articles with images, Wikipedia articles with images and Goats comic strips. In searching for similar images or textual information specified in a query, we explore the unification of textual descriptions and visual features (such as colour and texture) of the images. We compare the effectiveness of using different retrieval similarity measures for the textual component. We also analyse the effectiveness of different visual features extracted from the images. We then investigate the best weight combination of using textual and visual features. Using the queries from the Multimedia Track of INEX 2005 and 2006, we found that the best weight combination significantly improves the effectiveness of the retrieval system. Our findings suggest that image regions are better in capturing the semantics, since we can identify specific regions of interest in an image. In this context, we develop a technique to tag image regions with high-level semantics. This is done by combining several shape feature descriptors and colour, using an equal-weight linear combination. We experimentally compare this technique with more complex machine-learning algorithms, and show that the equal-weight linear combination of shape features is simpler and at least as effective as using a machine learning algorithm. We focus on the synergy between ontology and image annotations with the aim of reducing the gap between image features and high-level semantics. Ontologies ease information retrieval. They are used to mine, interpret, and organise knowledge. An ontology may be seen as a knowledge base that can be used to improve the image retrieval process, and conversely keywords obtained from automatic tagging of image regions may be useful for creating an ontology. We engineer an ontology that surrogates concepts derived from image feature descriptors. We test the usability of the constructed ontology by querying the ontology via the Visual Ontology Query Interface, which has a formally specified grammar known as the Visual Ontology Query Language. We show that synergy between ontology and image annotations is possible and this method can reduce the gap between image features and high-level semantics by providing the relationships between objects in the image. In this thesis, we conclude that suitable techniques for image retrieval include fusing text accompanying the images with visual features, automatic region tagging and using an ontology to enrich the semantic meaning of the tagged image regions

Fusion de systèmes et analyse des caractéristiques linguistiques des requêtes: vers un processus de RI adaptatif

Today, accessing wide volumes of information is reality. Information retrieval (IR) techniques are more and more used by a huge number of users on the Internet to retrieve relevant information (data, video, pictures, etc.). We are interested in this workin textual IR.Three elements are necessary during an IR process : an information need (more often a query of few words), an IR system and a set of documents. The query is submitted to the system which tries to return relevant documents from the set of document as an answer to the user inquiry. Variability in the expression of the query lead to variation in the performances of the systems (Buckley et al., 2004). For instance, system A can be very efficient for a given query and very bad for an other one, whereas system B gets opposite results.Or thesis is done in this context of variabilities. The main objective of our work is to propose retrieval techniques that can adapt to different contexts. We consider for example that the linguistic features of queries, the performance of the systems and theircharacteristics are contextual elements of the retrieval process. Many propositions are done in this thesis. Queries are clustered according to their linguistic features (Mothe et Tanguy, 2005) with technics like Agglomerative clustering methods and k-means. Queries are then analysed by the linguistic profile of their belonging cluster. The underlyinghypothesis is that some IR systems are more suitable than other for different clusters ofqueries. We analyse the performance of the systems for each of the determined cluster of queries (query context). Four fusion methods are proposed and tested with a set of experiments.This work is done in the context of TREC campain.La recherche d'information (RI) est un domaine de recherche qui est de plus en plus visible, surtout avec la profusion de données (textes, images, vidéos, etc) sur Internet.Nous nous intéressons dans cette thèse à la RI à partir de documents textuels non structurés.Trois éléments sont essentiels dans un processus de RI : un besoin d'information (généralement exprimé sous la forme d'une requête), un système de recherche d'information (SRI), et une collection de documents. Ainsi, la requête est soumise au SRI quirecherche dans la collection les documents les plus pertinents pour la requête. La variabilité relative à l'expression de la requête, la relation entre la requête et les documents, ainsi que celle liée aux caractéristiques des SRI utilisés conduisent à des variabilités dans les réponses obtenues (Buckley et al., 2004). Ainsi, le système A peut être trèsperformant pour une requête donnée et être très médiocre pour une autre requête, alors que le système B conduira à des résultats inversés.Notre thèse se situe dans ce contexte. Notre objectif est de proposer des méthodes de recherche pouvant s'intégrer dans un modèle de recherche capable de s'adapter à différents contextes. Nous considérons par exemple que les caractéristiques linguistiques (CL) des requêtes, les performances locales des systèmes ainsi que leurs caractéristiquessont des éléments définissant différents contextes. Nous proposons plusieurs processus afin d'atteindre cet objectif. D'une part, nous utilisons un profil linguistique des requêtes (Mothe et Tanguy, 2005) qui nous permet d'établir une classification des requêtes à base de leurs CL. Nous utilisons à cet effet des techniques statistiques d'analyse de données telles que la classification ascendante hiérarchique (CAH) et les k-means. Les requêtes ne sont plus alors considérées de manière isolée, mais sont vues comme des groupes possédant des CL similaires. L'hypothèse sous-jacente que nous faisons est qu'il existe des contextes dans lesquels certains SRI sont plus adaptés que d'autres. Nous étudions alors les performances des systèmes sur les classes de requêtes obtenues (contextes). Nous proposons quatre méthodes de fusion afin de combiner les résultats obtenus pour une requête donnée, par différents SRI. Une série d'expérimentations valide nos propositions. L'ensemble de ces travaux s'appuie sur l'évaluation au travers des campagnes d'évaluation de TREC