External query reformulation for text-based image retrieval

In text-based image retrieval, the Incomplete Annotation Problem (IAP) can greatly degrade retrieval effectiveness. A standard method used to address this problem is pseudo relevance feedback (PRF) which updates user queries by adding feedback terms selected automatically from top ranked documents in a prior retrieval run. PRF assumes that the target collection provides enough feedback information to select effective expansion terms. This is often not the case in image retrieval since images often only have short metadata annotations leading to the IAP. Our work proposes the use of an external knowledge resource (Wikipedia) in the process of refining user queries. In our method, Wikipedia documents strongly related to the terms in user query (" definition documents") are first identified by title matching between the query and titles of Wikipedia articles. These definition documents are used as indicators to re-weight the feedback documents from an initial search run on a Wikipedia abstract collection using the Jaccard coefficient. The new weights of the feedback documents are combined with the scores rated by different indicators. Query-expansion terms are then selected based on these new weights for the feedback documents. Our method is evaluated on the ImageCLEF WikipediaMM image retrieval task using text-based retrieval on the document metadata fields. The results show significant improvement compared to standard PRF methods

Mining document, concept, and term associations for effective biomedical retrieval - Introducing MeSH-enhanced retrieval models

Manually assigned subject terms, such as Medical Subject Headings (MeSH) in the health domain, describe the concepts or topics of a document. Existing information retrieval models do not take full advantage of such information. In this paper, we propose two MeSH-enhanced (ME) retrieval models that integrate the concept layer (i.e. MeSH) into the language modeling framework to improve retrieval performance. The new models quantify associations between documents and their assigned concepts to construct conceptual representations for the documents, and mine associations between concepts and terms to construct generative concept models. The two ME models reconstruct two essential estimation processes of the relevance model (Lavrenko and Croft 2001) by incorporating the document-concept and the concept-term associations. More specifically, in Model 1, language models of the pseudo-feedback documents are enriched by their assigned concepts. In Model 2, concepts that are related to users’ queries are first identified, and then used to reweight the pseudo-feedback documents according to the document-concept associations. Experiments carried out on two standard test collections show that the ME models outperformed the query likelihood model, the relevance model (RM3), and an earlier ME model. A detailed case analysis provides insight into how and why the new models improve/worsen retrieval performance. Implications and limitations of the study are discussed. This study provides new ways to formally incorporate semantic annotations, such as subject terms, into retrieval models. The findings of this study suggest that integrating the concept layer into retrieval models can further improve the performance over the current state-of-the-art models.Ye

An ontology-based concept search model for data repository with limited information

Many of the problems in natural language processing (NLP) or information retrieval (IR) stem from the rich expressive power in natural language. The use of concept search to overcome the limitations of keyword search has been put forward as one of the motivations of the Semantic Web since its emergence in the late 90\u27s. A lot of efforts have been made to adapt concept search principles for improving the performance of information retrieval systems. However most approaches are designed for data repositories which contain a large number of items, each with rich information. We propose a knowledge based concept search method that is particularly designed for the data repository with limited information items by narrowing down a query into one concept. Also, a framework adapting this method is proposed to solve the practical problem about how to extract the information from a specification document into an existing unstructured database. As an important part of the framework, a concept selection method using genetic algorithms and semantic distance is proposed to filter the meaningless or less important information in query generation and matching process. An application development process in mould engineering domain is introduced as a case study to show how to use this framework. The experiment results show our proposed concept search method performs better than classical keyword based search especially for the documents with ambiguous words and the concept selection method has a potential to further improve this concept search method

Investigating ontology based query expansion using a probabilistic retrieval model

This research briefly outlines the problems of traditional information retrieval systems and discusses the different approaches to inferring context in document retrieval. By context we mean word disambiguation which is achieved by exploring the generalisation-specialisation hierarchies within a given ontology. Specifically, we examine the use of ontology based query expansion for defining query context. Query expansion can be done in many ways and in this work we consider the use of relevance feedback and pseudo-relevance feedback for query expansion. We examine relevance feedback and pseudo-relevance to ascertain the existence of performance differences between relevance feedback and pseudo-relevance feedback. The information retrieval system used is based on the probabilistic retrieval model and the query expansion method is extended using information from a news domain ontology. The aim of this project is to assess the impact of the use of the ontology on the query expansion results. Our results show that ontology based query expansion has resulted in a higher number of relevant documents being retrieved compared to the standard relevance feedback process. Overall, ontology based query expansion improves recall but does not produce any significant improvements for the precision results. Pseudo-relevance feedback has achieved better results than relevance feedback. We also found that reducing or increasing the relevance feedback parameters (number of terms or number of documents) does not correlate with the results. When comparing the effect of varying the number of terms parameter with the number of documents parameter, the former benefits the pseudo-relevance feedback results but the latter has an additional effect on the relevance feedback results. There are many factors which influence the success of ontology based query expansion. The thesis discusses these factors and gives some guidelines on using ontologies for the purpose of query expansion

Accès à l'information biomédicale : vers une approche d'indexation et de recherche d'information conceptuelle basée sur la fusion de ressources termino-ontologiques

La recherche d'information (RI) est une discipline scientifique qui a pour objectif de produire des solutions permettant de sélectionner à partir de corpus d'information celle qui sont dites pertinentes pour un utilisateur ayant exprimé une requête. Dans le contexte applicatif de la RI biomédicale, les corpus concernent différentes sources d'information du domaine : dossiers médicaux de patients, guides de bonnes pratiques médicales, littérature scientifique du domaine médical etc. Les besoins en information peuvent concerner divers profils : des experts médicaux, des patients et leurs familles, des utilisateurs néophytes etc. Plusieurs défis sont liés spécifiquement à la RI biomédicale : la représentation "spécialisée" des documents, basés sur l'usage des ressources terminologiques du domaine, le traitement des synonymes, des acronymes et des abréviations largement pratiquée dans le domaine, l'accès à l'information guidé par le contexte du besoin et des profils des utilisateurs. Nos travaux de thèse s'inscrivent dans le domaine général de la RI biomédicale et traitent des défis de représentation de l'information biomédicale et de son accès. Sur le volet de la représentation de l'information, nous proposons des techniques d'indexation de documents basées sur : 1) la reconnaissance de concepts termino-ontologiques : cette reconnaissance s'apparente à une recherche approximative de concepts pertinents associés à un contenu, vu comme un sac de mots. La technique associée exploite à la fois la similitude structurelle des contenus informationnels des concepts vis-à-vis des documents mais également la similitude du sujet porté par le document et le concept, 2) la désambiguïsation des entrées de concepts reconnus en exploitant la branche liée au sous-domaine principal de la ressource termino-ontologique, 3) l'exploitation de différentes ressources termino-ontologiques dans le but de couvrir au mieux la sémantique du contenu documentaire. Sur le volet de l'accès à l'information, nous proposons des techniques d'appariement basées sur l'expansion combinée de requêtes et des documents guidées par le contexte du besoin en information d'une part et des contenus documentaires d'autre part. Notre analyse porte essentiellement sur l'étude de l'impact des différents paramètres d'expansion sur l'efficacité de la recherche : distribution des concepts dans les ressources ontologiques, modèle de fusion des concepts, modèle de pondération des concepts, etc. L'ensemble de nos contributions, en termes de techniques d'indexation et d'accès à l'information ont fait l'objet d'évaluation expérimentale sur des collections de test dédiées à la recherche d'information médicale, soit du point de vue de la tâche telles que TREC Medical track, CLEF Image, Medical case ou des collections de test telles que TREC Genomics.Information Retrieval (IR) is a scientific field aiming at providing solutions to select relevant information from a corpus of documents in order to answer the user information need. In the context of biomedical IR, there are different sources of information: patient records, guidelines, scientific literature, etc. In addition, the information needs may concern different profiles : medical experts, patients and their families, and other users ... Many challenges are specifically related to the biomedical IR : the document representation, the usage of terminologies with synonyms, acronyms, abbreviations as well as the access to the information guided by the context of information need and the user profiles. Our work is most related to the biomedical IR and deals with the challenges of the representation of biomedical information and the access to this rich source of information in the biomedical domain.Concerning the representation of biomedical information, we propose techniques and approaches to indexing documents based on: 1) recognizing and extracting concepts from terminologies : the method of concept extraction is basically based on an approximate lookup of candidate concepts that could be useful to index the document. This technique expoits two sources of evidence : (a) the content-based similarity between concepts and documents and (b) the semantic similarity between them. 2) disambiguating entry terms denoting concepts by exploiting the polyhierarchical structure of a medical thesaurus (MeSH - Medical Subject Headings). More specifically, the domains of each concept are exploited to compute the semantic similarity between ambiguous terms in documents. The most appropriate domain is detected and associated to each term denoting a particular concept. 3) exploiting different termino-ontological resources in an attempt to better cover the semantics of document contents. Concerning the information access, we propose a document-query matching method based on the combination of document and query expansion techniques. Such a combination is guided by the context of information need on one hand and the semantic context in the document on the other hand. Our analysis is essentially based on the study of factors related to document and query expansion that could have an impact on the IR performance: distribution of concepts in termino-ontological resources, fusion techniques for concept extraction issued from multiple terminologies, concept weighting models, etc