Fuzzy XPath : using fuzzy logic and IR features to approximately query XML documents

XML has become a key technology for interoperability, providing a common data model to applications. However, diverse data modeling choices may lead to heterogeneous XML structure and content. In this paper, information retrieval and database-related techniques have been jointly applied to effectively tolerate XML data diversity in the evaluation of flexible queries. Approximate structure and content matching is supported via a straightforward extension to standard XPath syntax. Also, we outline a query execution technique representing a first step toward efficiently addressing structural pattern queries together with predicate support over XML elements content

Toward IoT-Friendly Learning Models

In IoT environments, data are collected by many distinct devices, at the periphery, so that their feature-sets can be naturally endowed with a faceted structure. In this work, we argue that the IoT requires specialized ML models, able to exploit this faceted structure in the learning strategy. We demonstrate the application of this principle, by a multiple kernel learning approach, based on the exploration of the partition lattice driven by the natural partitioning of the feature set. Furthermore, we consider that the whole data management, acquisition, pre-processing and analytics pipeline results from the composition of processes pursuing different and non-perfectly aligned goals (most often, enacted by distinct agents with different constraints, requirements competencies and with non-aligned interests). We propose the adoption of an adversarial modeling paradigm across the overall pipeline. We argue that knowledge of the composite nature of the learning process, as well as of the adversarial character of the relationship among phases, can help in developing heuristics for improving the learning algorithms efficiency and accuracy. We develop our argument with reference to few exemplary use cases

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

Semantic Interaction in Web-based Retrieval Systems : Adopting Semantic Web Technologies and Social Networking Paradigms for Interacting with Semi-structured Web Data

Existing web retrieval models for exploration and interaction with web data do not take into account semantic information, nor do they allow for new forms of interaction by employing meaningful interaction and navigation metaphors in 2D/3D. This thesis researches means for introducing a semantic dimension into the search and exploration process of web content to enable a significantly positive user experience. Therefore, an inherently dynamic view beyond single concepts and models from semantic information processing, information extraction and human-machine interaction is adopted. Essential tasks for semantic interaction such as semantic annotation, semantic mediation and semantic human-computer interaction were identified and elaborated for two general application scenarios in web retrieval: Web-based Question Answering in a knowledge-based dialogue system and semantic exploration of information spaces in 2D/3D

Impact de la structure des documents XML sur le processus d'appariement dans le contexte de la recherche d'information semi-structurée

Nos travaux s'inscrivent dans le cadre de la recherche d'information sur documents semi-structurésde type XML. La recherche d'information structurée (RIS) a pour objectif de retourner des granules documentaires précis répondant aux besoins d'information exprimés par l'utilisateur au travers de requêtes. Ces requêtes permettent de spécifier, en plus des conditions de contenu, des contraintes structurelles sur la localisation de l'information recherchée. L'objectif de nos travaux est d'étudier l'apport de la structure des documents dans le processus d'appariement documents-requêtes. Puisque les contraintes structurelles des requêtes peuvent être représentées sous la forme d'un arbre et que, parallèlement, la structure du document, de nature hiérarchique, peut elle-même utiliser le même type de représentation, nous avons proposé plusieurs modèles de mesure de la similarité entre ces deux structures. La mesure de la similarité entre deux structures arborescentes ayant été étudiée par le domaine de la théorie des graphes, nous avons tout d'abord cherché à adapter les algorithmes de ce domaine à notre problématique. Suite à une étude approfondie de ces algorithmes au regard de la RIS, notre choix s'est porté sur la distance d'édition entre arbres (Tree Edit Distance - TED). Cet algorithme permet, au travers de l'application récursive de séquences de suppression et de substitution, de mesurer le degré d'isomorphisme (le degré de similarité) entre deux arbres. Constatant que ces algorithmes sont coûteux en mémoire et en calcul, nous avons cherché à en réduire la complexité et le temps d'exécution au travers d'approches de résumé et de la mise en place d'un algorithme de TED au coût de complexité plus bas. Etant donné que la TED est normalement utilisée avec des coûts d'opération fixes peut adaptés à notre problématique, nous en avons également proposé de nouveaux basés sur la distance dans le graphe formé par la grammaire des documents : la DTD. Notre deuxième proposition se base sur les Modèles de Langue. En recherche d'information, ces derniers sont utilisés afin de mesurer la pertinence au travers de la probabilité qu'un terme de la requête soit généré par un document. Nous avons utilisés les Modèles de Langue pour mesurer, non pas la probabilité de pertinence du contenu, mais celle de la structure. Afin de former un vocabulaire document et requête à même d'être utilisé par notre modèle de langue structurel nous avons utilisé une technique de relaxation pondérée (la relaxation est le relâchement des contraintes). Nous avons également proposé une méthode pour apparier le contenu des documents et celui des requêtes. L'appariement seul des structures étant insuffisant dans une problématique de recherche d'information : la pertinence d'un granule documentaire est jugée en priorité sur la pertinence de l'information textuelle qu'il contient. De ce fait, nous avons proposé une approche de mesure de la pertinence de ce contenu. Notre méthode utilise la structure de l'arbre afin d'opérer une propagation de la pertinence du texte en prenant en compte l'environnement des éléments traversés ainsi que le contexte global du document. Nos différents modèles ont été expérimentés sur deux tâches de la campagne d'évaluation de référence de notre domaine : Initiative for XML Retrieval. Cette campagne a pour but de permettre l'évaluation de systèmes de recherche d'information XML dans un cadre normalisée et comporte plusieurs tâches fournissant des corpus, des mesures d'évaluation, des requêtes, et des jugements de pertinence. Nous avons à ce propos participé à cette campagne en 2011.Pour nos expérimentations, les tâches que nous avons choisi d'utiliser sont : * La tâche SSCAS d'INEX 2005 qui utilise une collection d'articles scientifiques d'IEEE. Cette collection est orientée texte dans la mesure où la structure exprimée dans les documents qu'elle contient est similaire à celle d'un livre (paragraphe, sections). * La tâche Datacentric d'INEX 2010 dont la collection est extraite d'IMDB. Cette collection est orientée données dans la mesure où les termes des documents sont très spécifiques et peu redondants et que la structure est porteuse de sens. Nos différentes expérimentations nous ont permis de montrer que le choix de la méthode d'appariement dépend de la collection considérée. Dans le cadre d'une collection orienté texte, la structure peut être prise en compte de manière non stricte et plusieurs sous-arbres extraits du document peuvent être utilisés simultanément pour évaluer la similarité structurelle. Inversement, dans le cadre d'une collection orientée donnée, la prise en compte stricte de la structure est nécessaire. Etant donné que les éléments recherchés portent une sémantique, il est alors important de détecter quelle partie du document est à priori pertinente. La structure à apparier doit être la plus précise et minimale possible. Enfin, nos approches de mesures de la similarité structurelle se sont montrées performantes et ont amélioré la pertinence des résultats retournés par rapport à l'état de l'art, à partir du moment où la nature de la collection a été prise en compte dans la sélection des arbres structurels en entrée.The work presented in this PhD thesis concerns structured information retrieval and focuses on XML documents. Structured information retrieval (SIR) aims at returning to users document parts (instead of whole documents) relevant to their needs. Those needs are expressed by queries that can contain content conditions as well as structural constraints which are used to specify the location of the needed information. In this work, we are interested in the use of document structure in the retrieval process. We propose some approaches to evaluate the document-query structural similarity. Both query structural constraints and document structures can be represented as trees. Based on this observation we propose two models which aim at matching these tree structures. As tree matching is historically linked with graph theory, our first proposition is based on an adaptation of a solution from the graph theory. After conducting an in depth study of the existing graph theory algorithms, we choose to use Tree Edit Distance (TED), which measures isomorphism (tree similarity) as the minimal set of remove and replace operations to turn one tree to another. As the main drawback of TED algorithms is their time and space complexity, which impacts the overall matching runtime, we propose two ways to overcome these issues. First we propose a TED algorithm having a minimal space complexity overall. Secondly, as runtime is dependent on the input tree cardinality (size) we propose several summarization techniques. Finally, since TED is usually used to assess relatively similar trees and as TED efficiency strongly relies on its costs, we propose a novel way, based on the DTD of documents, to compute these costs. Our second proposition is based on language models which are considered as very effective IR models. Traditionally, they are use to assess the content similarity through the probability of a document model (build upon document terms) to generate the query. We take a different approach based purely on structure and consider the document and query vocabulary as a set of transitions between document structure labels. To build these vocabularies, we propose to extract and weight all the structural relationships through a relaxation process. Finally, as relevance of the returned search results is first assessed based on the content, we propose a content evaluation process which uses the document tree structure to propagate relevance: the relevance of a node is evaluated thanks to its leaves as well as with the document context and neighbour nodes content relevance. In order to validate our models we conduct some experiments on two data-sets from the reference evaluation campaign of our domain: Initiative for XML retrieval (INEX). INEX tracks provide documents collections, metrics and relevance judgments which can be used to assess and compare SIR models. The tracks we use are: * The INEX 2005 SSCAS track whose associated documents are scientific papers extracted from IEEE. We consider this collection to be text-oriented as the structure used is similar to the one we can find in a book. * The INEX 2010 Datacentric track which uses a set of documents extracted from the Internet Movie Database (IMDB) website. This collection is data-oriented as document terms are very specific while the structure carries semantic meaning. Our various experiments show that the matching strategy strongly relies on the document structure type. In text-oriented collections, the structure can be considered as non-strict and several subtrees can be simultaneously used to assess the relevance. On the opposite, structure from documents regarded as data-centered should be used as strictly as possible. The reason is that as elements labels carry semantic, documents structures contain relevant and useful information that the content does not necessarily provide. Finally, our structural similarity approaches improve relevance of the returned results compared to state-of-the-art approaches, as long as the collection nature is considered when extracting the input trees for the structural matching process

An Ontology-Driven Sociomedical Web 3.0 Framework

Web 3.0, the web of social and semantic cooperation, calls for a methodological multidisciplinary architecture in order to reach its mainstream objectives. With the lack of such an architecture and the reliance of existing efforts on lightweight semantics and RDF graphs, this thesis proposes "Web3.OWL", an ontology-driven framework towards a Web 3.0 knowledge architecture. Meanwhile, the online social parenting data and their corresponding websites users known as "mommy bloggers" undergo one of the fastest online demographics growth, and the available literature reflects the very little attention this growth has so far been given and the various deficiencies the parenting domain suffers from; these deficiencies all fall under the umbrella of the scarcity of parenting sociomedical analysis and decision-support systems. The Web3.OWL framework puts forward an approach that relies on the Meta-Object Facility for Semantics standard (SMOF) for the management of its modeled OWL (Web Ontology Language) expressive domain ontologies on the one hand, and the coordination of its various underlined Web 3.0 prerequisite disciplines on the other. Setting off with a holistic portrayal of Web3.OWL’s components and workflow, the thesis progresses into a more analytic exploration of its main paradigms. Out of its different ontology-aware paradigms are notably highlighted both its methodology for expressiveness handling through modularization and projection techniques and algorithms, and its facilities for tagging inference, suggestion and processing. Web3.OWL, albeit generic by conception, proves its efficiency in solving the deficiencies and meeting the requirements of the sociomedical domain of interest. Its conceived ontology for parenting analysis and surveillance, baptised "ParOnt", strongly contributes to the backbone metamodel and the various constituents of this ontology-driven framework. Accordingly, as the workflow revolves around Description Logics principles, OWL 2 profiles along with standard and beyond-standard reasoning techniques, conducted experiments and competency questions are illustrated, thus establishing the required Web 3.0 outcomes. The empirical results of the diverse preliminary decision-support and recommendation services targeting parenting public awareness, orientation and education do ascertain, in conclusion, the value and potentials of the proposed conceptual framework

A POWER INDEX BASED FRAMEWORKFOR FEATURE SELECTION PROBLEMS

One of the most challenging tasks in the Machine Learning context is the feature selection. It consists in selecting the best set of features to use in the training and prediction processes. There are several benefits from pruning the set of actually operational features: the consequent reduction of the computation time, often a better quality of the prediction, the possibility to use less data to create a good predictor. In its most common form, the problem is called single-view feature selection problem, to distinguish it from the feature selection task in Multi-view learning. In the latter, each view corresponds to a set of features and one would like to enact feature selection on each view, subject to some global constraints. A related problem in the context of Multi-View Learning, is Feature Partitioning: it consists in splitting the set of features of a single large view into two or more views so that it becomes possible to create a good predictor based on each view. In this case, the best features must be distributed between the views, each view should contain synergistic features, while features that interfere disruptively must be placed in different views. In the semi-supervised multi-view task known as Co-training, one requires also that each predictor trained on an individual view is able to teach something to the other views: in classification tasks for instance, one view should learn to classify unlabelled examples based on the guess provided by the other views. There are several ways to address these problems. A set of techniques is inspired by Coalitional Game Theory. Such theory defines several useful concepts, among which two are of high practical importance: the concept of power index and the concept of interaction index. When used in the context of feature selection, they take the following meaning: the power index is a (context-dependent) synthesis measure of the prediction\u2019s capability of a feature, the interaction index is a (context-dependent) synthesis measure of the interaction (constructive/disruptive interference) between two features: it can be used to quantify how the collaboration between two features enhances their prediction capabilities. An important point is that the powerindex of a feature is different from the predicting power of the feature in isolation: it takes into account, by a suitable averaging, the context, i.e. the fact that the feature is acting, together with other features, to train a model. Similarly, the interaction index between two features takes into account the context, by suitably averaging the interaction with all the other features. In this work we address both the single-view and the multi-view problems as follows. The single-view feature selection problem, is formalized as the problem of maximization of a pseudo-boolean function, i.e. a real valued set function (that maps sets of features into a performance metric). Since one has to enact a search over (a considerable portion of) the Boolean lattice (without any special guarantees, except, perhaps, positivity) the problem is in general NP-hard. We address the problem producing candidate maximum coalitions through the selection of the subset of features characterized by the highest power indices and using the coalition to approximate the actual maximum. Although the exact computation of the power indices is an exponential task, the estimates of the power indices for the purposes of the present problem can be achieved in polynomial time. The multi-view feature selection problem is formalized as the generalization of the above set-up to the case of multi-variable pseudo-boolean functions. The multi-view splitting problem is formalized instead as the problem of maximization of a real function defined over the partition lattice. Also this problem is typically NP-hard. However, candidate solutions can be found by suitably partitioning the top power-index features and keeping in different views the pairs of features that are less interactive or negatively interactive. The sum of the power indices of the participating features can be used to approximate the prediction capability of the view (i.e. they can be used as a proxy for the predicting power). The sum of the feature pair interactivity across views can be used as proxy for the orthogonality of the views. Also the capability of a view to pass information (to teach) to other views, within a co-training procedure can benefit from the use of power indices based on a suitable definition of information transfer (a set of features { a coalition { classifies examples that are subsequently used in the training of a second set of features). As to the feature selection task, not only we demonstrate the use of state of the art power index concepts (e.g. Shapley Value and Banzhaf along the 2lines described above Value), but we define new power indices, within the more general class of probabilistic power indices, that contains the Shapley and the Banzhaf Values as special cases. Since the number of features to select is often a predefined parameter of the problem, we also introduce some novel power indices, namely k-Power Index (and its specializations k-Shapley Value, k-Banzhaf Value): they help selecting the features in a more efficient way. For the feature partitioning, we use the more general class of probabilistic interaction indices that contains the Shapley and Banzhaf Interaction Indices as members. We also address the problem of evaluating the teaching ability of a view, introducing a suitable teaching capability index. The last contribution of the present work consists in comparing the Game Theory approach to the classical Greedy Forward Selection approach for feature selection. In the latter the candidate is obtained by aggregating one feature at time to the current maximal coalition, by choosing always the feature with the maximal marginal contribution. In this case we show that in typical cases the two methods are complementary, and that when used in conjunction they reduce one another error in the estimate of the maximum value. Moreover, the approach based on game theory has two advantages: it samples the space of all possible features\u2019 subsets, while the greedy algorithm scans a selected subspace excluding totally the rest of it, and it is able, for each feature, to assign a score that describes a context-aware measure of importance in the prediction process

Integrated topological representation of multi-scale utility resource networks

PhD ThesisThe growth of urban areas and their resource consumption presents a significant global challenge. Existing utility resource supply systems are unresponsive, unreliable and costly. There is a need to improve the configuration and management of the infrastructure networks that carry these resources from source to consumer and this is best performed through analysis of multi-scale, integrated digital representations. However, the real-world networks are represented across different datasets that are underpinned by different data standards, practices and assumptions, and are thus challenging to integrate. Existing integration methods focus predominantly on achieving maximum information retention through complex schema mappings and the development of new data standards, and there is strong emphasis on reconciling differences in geometries. However, network topology is of greatest importance for the analysis of utility networks and simulation of utility resource flows because it is a representation of functional connectivity, and the derivation of this topology does not require the preservation of full information detail. The most pressing challenge is asserting the connectivity between the datasets that each represent subnetworks of the entire end-to-end network system. This project presents an approach to integration that makes use of abstracted digital representations of electricity and water networks to infer inter-dataset network connectivity, exploring what can be achieved by exploiting commonalities between existing datasets and data standards to overcome their otherwise inhibiting disparities. The developed methods rely on the use of graph representations, heuristics and spatial inference, and the results are assessed using surveying techniques and statistical analysis of uncertainties. An algorithm developed for water networks was able to correctly infer a building connection that was absent from source datasets. The thesis concludes that several of the key use cases for integrated topological representation of utility networks are partially satisfied through the methods presented, but that some differences in data standardisation and best practice in the GIS and BIM domains prevent full automation. The common and unique identification of real-world objects, agreement on a shared concept vocabulary for the built environment, more accurate positioning of distribution assets, consistent use of (and improved best practice for) georeferencing of BIM models and a standardised numerical expression of data uncertainties are identified as points of development.Engineering and Physical Sciences Research Council Ordnance Surve

Fuzzy Techniques for XML Data Smushing

The recently proposed notion of a Semantic Web requires XML/RDF processing techniques able to locate, extract and organise heterogeneous information contained in XML documents coming from different sites, dealing flexibly with differences in structure and tag vocabulary. Such techniques should operate even when tagging is done in accordance with non-informative schemata, and even when no schema is available at all. In this paper, we review the main problems related to the processing and restructuring of large amounts of XML-based data, and propose some solutions in the framework of a flexible query and processing model for well-formed XML documents

Fuzzy Techniques for XML Data Smushing

The recently proposed notion of a Semantic Web requires XML/RDF processing techniques able to locate, extract and organise heterogeneous information contained in XML documents coming from different sites, dealing flexibly with differences in structure and tag vocabulary. Such techniques should operate even when tagging is done in accordance with non-informative schemata, and even when no schema is available at all. In this paper, we review the main problems related to the processing and restructuring of large amounts of XML-based data, and propose some solutions in the framework of a flexible query and processing model for well-formed XML documents