Investigating the document structure as a source of evidence for multimedia fragment retrieval

International audienceMultimedia objects can be retrieved using their context that can be for instance the text surrounding them in documents. This text may be either near or far from the searched objects. Our goal in this paper is to study the impact, in term of effectiveness, of text position relatively to searched objects. The multimedia objects we consider are described in structured documents such as XML ones. The document structure is therefore exploited to provide this text position in documents. Although structural information has been shown to be an effective source of evidence in textual information retrieval, only a few works investigated its interest in multimedia retrieval. More precisely, the task we are interested in this paper is to retrieve multimedia fragments (i.e. XML elements having at least one multimedia object). Our general approach is built on two steps: we first retrieve XML elements containing multimedia objects, and we then explore the surrounding information to retrieve relevant multimedia fragments. In both cases, we study the impact of the surrounding information using the documents structure.Our work is carried out on images, but it can be extended to any other media, since the physical content of multimedia objects is not used. We conducted several experiments in the context of the Multimedia track of the INEX evaluation campaign. Results showed that structural evidences are of high interest to tune the importance of textual context for multimedia retrieval. Moreover, the proposed approach outperforms state of the art approaches

A survey on tree matching and XML retrieval

International audienceWith the increasing number of available XML documents, numerous approaches for retrieval have been proposed in the literature. They usually use the tree representation of documents and queries to process them, whether in an implicit or explicit way. Although retrieving XML documents can be considered as a tree matching problem between the query tree and the document trees, only a few approaches take advantage of the algorithms and methods proposed by the graph theory. In this paper, we aim at studying the theoretical approaches proposed in the literature for tree matching and at seeing how these approaches have been adapted to XML querying and retrieval, from both an exact and an approximate matching perspective. This study will allow us to highlight theoretical aspects of graph theory that have not been yet explored in XML retrieval

Random Indexing K-tree

Random Indexing (RI) K-tree is the combination of two algorithms for clustering. Many large scale problems exist in document clustering. RI K-tree scales well with large inputs due to its low complexity. It also exhibits features that are useful for managing a changing collection. Furthermore, it solves previous issues with sparse document vectors when using K-tree. The algorithms and data structures are defined, explained and motivated. Specific modifications to K-tree are made for use with RI. Experiments have been executed to measure quality. The results indicate that RI K-tree improves document cluster quality over the original K-tree algorithm.Comment: 8 pages, ADCS 2009; Hyperref and cleveref LaTeX packages conflicted. Removed clevere

Users and entities on the web

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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

From people to entities : typed search in the enterprise and the web

[no abstract

KMI, The Open University at NTCIR-9 CrossLink: Cross-Lingual Link Discovery in Wikipedia using explicit semantic analysis

This paper describes the methods used in the submission of Knowledge Media institute (KMI), The Open University to the NTCIR-9 Cross-Lingual Link Discovery (CLLD)task entitled CrossLink. KMI submitted four runs for link discovery from English to Chinese; however, the developed methods, which utilise Explicit Semantic Analysis (ESA), are applicable also to other language combinations. Three of the runs are based on exploiting the existing cross-lingual mapping between different versions of Wikipedia articles. In the fourth run, we assume information about the mapping is not available. Our methods achieved encouraging results and we describe in detail how their performance can be further improved. Finally, we discuss two important issues in link discovery: the evaluation methodology and the applicability of the developed methods across dfferent textual collections

Language Models and Smoothing Methods for Information Retrieval

Language Models and Smoothing Methods for Information Retrieval (Sprachmodelle und Glättungsmethoden für Information Retrieval) Najeeb A. Abdulmutalib Kurzfassung der Dissertation Retrievalmodelle bilden die theoretische Grundlage für effektive Information-Retrieval-Methoden. Statistische Sprachmodelle stellen eine neue Art von Retrievalmodellen dar, die seit etwa zehn Jahren in der Forschung betrachtet werde. Im Unterschied zu anderen Modellen können sie leichter an spezifische Aufgabenstellungen angepasst werden und liefern häufig bessere Retrievalergebnisse. In dieser Dissertation wird zunächst ein neues statistisches Sprachmodell vorgestellt, das explizit Dokumentlängen berücksichtigt. Aufgrund der spärlichen Beobachtungsdaten spielen Glättungsmethoden bei Sprachmodellen eine wichtige Rolle. Auch hierfür stellen wir eine neue Methode namens 'exponentieller Glättung' vor. Der experimentelle Vergleich mit konkurrierenden Ansätzen zeigt, dass unsere neuen Methoden insbesondere bei Kollektionen mit stark variierenden Dokumentlängen überlegene Ergebnisse liefert. In einem zweiten Schritt erweitern wir unseren Ansatz auf XML-Retrieval, wo hierarchisch strukturierte Dokumente betrachtet werden und beim fokussierten Retrieval möglichst kleine Dokumentteile gefunden werden sollen, die die Anfrage vollständig beantworten. Auch hier demonstriert der experimentelle Vergleich mit anderen Ansätzen die Qualität unserer neu entwickelten Methoden. Der dritte Teil der Arbeit beschäftigt sich mit dem Vergleich von Sprachmodellen und der klassischen tf*idf-Gewichtung. Neben einem besseren Verständnis für die existierenden Glättungsmethoden führt uns dieser Ansatz zur Entwicklung des Verfahrens der 'empirischen Glättung'. Die damit durchgeführten Retrievalerexperimente zeigen Verbesserungen gegenüber anderen Glättungsverfahren.Language Models and Smoothing Methods for Information Retrieval Najeeb A. Abdulmutalib Abstract of the Dissertation Designing an effective retrieval model that can rank documents accurately for a given query has been a central problem in information retrieval for several decades. An optimal retrieval model that is both effective and efficient and that can learn from feedback information over time is needed. Language models are new generation of retrieval models and have been applied since the last ten years to solve many different information retrieval problems. Compared with the traditional models such as the vector space model, they can be more easily adapted to model non traditional and complex retrieval problems and empirically they tend to achieve comparable or better performance than the traditional models. Developing new language models is currently an active research area in information retrieval. In the first stage of this thesis we present a new language model based on an odds formula, which explicitly incorporates document length as a parameter. To address the problem of data sparsity where there is rarely enough data to accurately estimate the parameters of a language model, smoothing gives a way to combine less specific, more accurate information with more specific, but noisier data. We introduce a new smoothing method called exponential smoothing, which can be combined with most language models. We present experimental results for various language models and smoothing methods on a collection with large document length variation, and show that our new methods compare favourably with the best approaches known so far. We discuss the collection effect on the retrieval function, where we investigate the performance of well known models and compare the results conducted using two variant collections. In the second stage we extend the current model from flat text retrieval to XML retrieval since there is a need for content-oriented XML retrieval systems that can efficiently and effectively store, search and retrieve information from XML document collections. Compared to traditional information retrieval, where whole documents are usually indexed and retrieved as single complete units, information retrieval from XML documents creates additional retrieval challenges. By exploiting the logical document structure, XML allows for more focussed retrieval that identifies elements rather than documents as answers to user queries. Finally we show how smoothing plays a role very similar to that of the idf function: beside the obvious role of smoothing, it also improves the accuracy of the estimated language model. The within document frequency and the collection frequency of a term actually influence the probability of relevance, which led us to a new class of smoothing function based on numeric prediction, which we call empirical smoothing. Its retrieval quality outperforms that of other smoothing methods

Recherche d'information et contexte

My research work is related the field of Information Retrieval (IR) whose objective is to enable a user to find information that meets its needs within a large volume of information. The work in IR have focused primarily on improving information processing in terms of indexing to obtain optimal representations of documents and queries and in terms of matching between these representations. Contributions have long made no distinction between all searches assuming a unique type of search and when proposing a model intended to be effective for this unique type of search. The growing volume of information and diversity of situations have marked the limits of existing IR approaches bringing out the field of contextual IR. Contextual IR aims to better respond to users' needs taking into account the search context. The principle is to differentiate searches by integrating in the IR process, contextual factors that will influence the IRS effectiveness. The notion of context is broad and refers to all knowledge related to information conducted by a user querying an IRS. My research has been directed toward taking into account the contextual factors that are: the domain of information, the information structure and the user. The first three directions of my work consist in proposing models that incorporate each of these elements of context, and a fourth direction aims at exploring how to adapt the process to each search according to its context. Various European and national projects have provided application frameworks for this research and have allowed us to validate our proposals. This research has also led to development of various prototypes and allowed the conduct of PhD theses and research internships.Mes travaux de recherche s'inscrivent dans le domaine de la recherche d'information (RI) dont l'objectif est de permettre à un utilisateur de trouver de l'information répondant à son besoin au sein d'un volume important d'informations. Les recherches en RI ont été tout d'abord orientées système. Elles sont restées très longtemps axées sur l'appariement pour évaluer la correspondance entre les requêtes et les documents ainsi que sur l'indexation des documents et de requêtes pour obtenir une représentation qui supporte leur mise en correspondance. Cela a conduit à la définition de modèles théoriques de RI comme le modèle vectoriel ou le modèle probabiliste. L'objectif initialement visé a été de proposer un modèle de RI qui possède un comportement global le plus efficace possible. La RI s'est longtemps basée sur des hypothèses simplificatrices notamment en considérant un type unique d'interrogation et en appliquant le même traitement à chaque interrogation. Le contexte dans lequel s'effectue la recherche a été ignoré. Le champ d'application de la RI n'a cessé de s'étendre notamment grâce à l'essor d'internet. Le volume d'information toujours plus important combiné à une utilisation de SRI qui s'est démocratisée ont conduit à une diversité des situations. Cet essor a rendu plus difficile l'identification des informations correspondant à chaque besoin exprimé par un utilisateur, marquant ainsi les limites des approches de RI existantes. Face à ce constat, des propositions ont émergé, visant à faire évoluer la RI en rapprochant l'utilisateur du système tels que les notions de réinjection de pertinence utilisateur ou de profil utilisateur. Dans le but de fédérer les travaux et proposer des SRI offrant plus de précision en réponse au besoin de l'utilisateur, le domaine de la RI contextuelle a récemment émergé. L'objectif est de différencier les recherches au niveau des modèles de RI en intégrant des éléments de contexte susceptibles d'avoir une influence sur les performances du SRI. La notion de contexte est vaste et se réfère à toute connaissance liée à la recherche de l'utilisateur interrogeant un SRI. Mes travaux de recherche se sont orientés vers la prise en compte des éléments de contexte que sont le domaine de l'information, la structure de l'information et l'utilisateur. Ils consistent, dans le cadre de trois premières orientations, à proposer des modèles qui intègrent chacun de ces éléments de contexte, et, dans une quatrième orientation, d'étudier comment adapter les processus à chaque recherche en fonction de son contexte. Différents projets européens et nationaux ont servi de cadre applicatifs à ces recherches et ainsi à valider nos propositions. Mes travaux de recherche ont également fait l'objet de développements dans différents prototypes et ont permis le déroulement de thèses de doctorat et stages de recherche