Query Processing on the Entity-Relationship Graph Based Relational Database Systems.

An ERG (Entity-Relationship Graph) can be used to provide a semantic structure to a relational database system. An ERG is defined by local regions. A local region contains two nodes of entity types and a node of relationship type. The semantic constraints of the database represented by the ERG (Entity-Relationship Graph) can be used to enforce the global integrity of the database system. A query is mapped onto the ERG to obtain an ERQG (Entity-Relationship Query Graph). This mapping can be specified by the user by navigating the database or automatically allocated by the system via a universal relation interface. The ERQG representation of a query can be semantically decomposed into a sequence of Local Regions. These Local Regions can then be processed according to their order in the query. The ER-semijoin operation is introduced to process this sequence of Local Regions. Using this approach, architectures of database systems are proposed--two-phase interface and one-phase interface. An implementation of a user interface is also discussed

Questions-Réponses en domaine ouvert (sélection pertinente de documents en fonction du contexte de la question)

Les problématiques abordées dans ma thèse sont de définir une adaptation unifiée entre la sélection des documents et les stratégies de recherche de la réponse à partir du type des documents et de celui des questions, intégrer la solution au système de Questions-Réponses (QR) RITEL du LIMSI et évaluer son apport. Nous développons et étudions une méthode basée sur une approche de Recherche d Information pour la sélection de documents en QR. Celle-ci s appuie sur un modèle de langue et un modèle de classification binaire de texte en catégorie pertinent ou non pertinent d un point de vue QR. Cette méthode permet de filtrer les documents sélectionnés pour l extraction de réponses par un système QR. Nous présentons la méthode et ses modèles, et la testons dans le cadre QR à l aide de RITEL. L évaluation est faite en français en contexte web sur un corpus de 500 000 pages web et de questions factuelles fournis par le programme Quaero. Celle-ci est menée soit sur des documents complets, soit sur des segments de documents. L hypothèse suivie est que le contenu informationnel des segments est plus cohérent et facilite l extraction de réponses. Dans le premier cas, les gains obtenus sont faibles comparés aux résultats de référence (sans filtrage). Dans le second cas, les gains sont plus élevés et confortent l hypothèse, sans pour autant être significatifs. Une étude approfondie des liens existant entre les performances de RITEL et les paramètres de filtrage complète ces évaluations. Le système de segmentation créé pour travailler sur des segments est détaillé et évalué. Son évaluation nous sert à mesurer l impact de la variabilité naturelle des pages web (en taille et en contenu) sur la tâche QR, en lien avec l hypothèse précédente. En général, les résultats expérimentaux obtenus suggèrent que notre méthode aide un système QR dans sa tâche. Cependant, de nouvelles évaluations sont à mener pour rendre ces résultats significatifs, et notamment en utilisant des corpus de questions plus importants.This thesis aims at defining a unified adaptation of the document selection and answer extraction strategies, based on the document and question types, in a Question-Answering (QA) context. The solution is integrated in RITEL (a LIMSI QA system) to assess the contribution. We develop and investigate a method based on an Information Retrieval approach for the selection of relevant documents in QA. The method is based on a language model and a binary model of textual classification in relevant or irrelevant category. It is used to filter unusable documents for answer extraction by matching lists of a priori relevant documents to the question type automatically. First, we present the method along with its underlying models and we evaluate it on the QA task with RITEL in French. The evaluation is done on a corpus of 500,000 unsegmented web pages with factoid questions provided by the Quaero program (i.e. evaluation at the document level or D-level). Then, we evaluate the methodon segmented web pages (i.e. evaluation at the segment level or S-level). The idea is that information content is more consistent with segments, which facilitates answer extraction. D-filtering brings a small improvement over the baseline (no filtering). S-filtering outperforms both the baseline and D-filtering but not significantly. Finally, we study at the S-level the links between RITEL s performances and the key parameters of the method. In order to apply the method on segments, we created a system of web page segmentation. We present and evaluate it on the QA task with the same corpora used to evaluate our document selection method. This evaluation follows the former hypothesis and measures the impact of natural web page variability (in terms of size and content) on RITEL in its task. In general, the experimental results we obtained suggest that our IR-based method helps a QA system in its task, however further investigations should be conducted especially with larger corpora of questions to make them significant.PARIS11-SCD-Bib. électronique (914719901) / SudocSudocFranceF

Why-Query Support in Graph Databases

In the last few decades, database management systems became powerful tools for storing large amount of data and executing complex queries over them. In addition to extended functionality, novel types of databases appear like triple stores, distributed databases, etc. Graph databases implementing the property-graph model belong to this development branch and provide a new way for storing and processing data in the form of a graph with nodes representing some entities and edges describing connections between them. This consideration makes them suitable for keeping data without a rigid schema for use cases like social-network processing or data integration. In addition to a flexible storage, graph databases provide new querying possibilities in the form of path queries, detection of connected components, pattern matching, etc. However, the schema flexibility and graph queries come with additional costs. With limited knowledge about data and little experience in constructing the complex queries, users can create such ones, which deliver unexpected results. Forced to debug queries manually and overwhelmed by the amount of query constraints, users can get frustrated by using graph databases. What is really needed, is to improve usability of graph databases by providing debugging and explaining functionality for such situations. We have to assist users in the discovery of what were the reasons of unexpected results and what can be done in order to fix them. The unexpectedness of result sets can be expressed in terms of their size or content. In the first case, users have to solve the empty-answer, too-many-, or too-few-answers problems. In the second case, users care about the result content and miss some expected answers or wonder about presence of some unexpected ones. Considering the typical problems of receiving no or too many results by querying graph databases, in this thesis we focus on investigating the problems of the first group, whose solutions are usually represented by why-empty, why-so-few, and why-so-many queries. Our objective is to extend graph databases with debugging functionality in the form of why-queries for unexpected query results on the example of pattern matching queries, which are one of general graph-query types. We present a comprehensive analysis of existing debugging tools in the state-of-the-art research and identify their common properties. From them, we formulate the following features of why-queries, which we discuss in this thesis, namely: holistic support of different cardinality-based problems, explanation of unexpected results and query reformulation, comprehensive analysis of explanations, and non-intrusive user integration. To support different cardinality-based problems, we develop methods for explaining no, too few, and too many results. To cover different kinds of explanations, we present two types: subgraph- and modification-based explanations. The first type identifies the reasons of unexpectedness in terms of query subgraphs and delivers differential graphs as answers. The second one reformulates queries in such a way that they produce better results. Considering graph queries to be complex structures with multiple constraints, we investigate different ways of generating explanations starting from the most general one that considers only a query topology through coarse-grained rewriting up to fine-grained modification that allows fine changes of predicates and topology. To provide a comprehensive analysis of explanations, we propose to compare them on three levels including a syntactic description, a content, and a size of a result set. In order to deliver user-aware explanations, we discuss two models for non-intrusive user integration in the generation process. With the techniques proposed in this thesis, we are able to provide fundamentals for debugging of pattern-matching queries, which deliver no, too few, or too many results, in graph databases implementing the property-graph model