Discovery-driven ontology evolution

In this paper, we present a methodology for ontology evolution, by focusing on the specific case of multimedia ontology volution. In particular, we discuss the situation where the ontology needs to be enriched because it does not contain any concept that could be used to explain a new multimedia resource. The paper shows how ontology matching techniques can be used to enforce the discovery of new relevant concepts by probing external knowledge sources using both the information available in the multimedia resource and the knowledge contained in the current version of the ontology

Dynamic Hyperlinker: Innovative Solution for 3D Video Content Search and Retrieval

Recently, 3D display technology, and content creation tools have been undergone rigorous development and as a result they have been widely adopted by home and professional users. 3D digital repositories are increasing and becoming available ubiquitously. However, searching and visualizing 3D content remains a great challenge. In this paper, we propose and present the development of a novel approach for creating hypervideos, which ease the 3D content search and retrieval. It is called the dynamic hyperlinker for 3D content search and retrieval process. It advances 3D multimedia navigability and searchability by creating dynamic links for selectable and clickable objects in the video scene whilst the user consumes the 3D video clip. The proposed system involves 3D video processing, such as detecting/tracking clickable objects, annotating objects, and metadata engineering including 3D content descriptive protocol. Such system attracts the attention from both home and professional users and more specifically broadcasters and digital content providers. The experiment is conducted on full parallax holoscopic 3D videos “also known as integral images”.ICT program as Project 3D VIVAN

KD SENSO-MERGER: An architecture for semantic integration of heterogeneous data

This paper presents KD SENSO-MERGER, a novel Knowledge Discovery (KD) architecture that is capable of semantically integrating heterogeneous data from various sources of structured and unstructured data (i.e. geolocations, demographic, socio-economic, user reviews, and comments). This goal drives the main design approach of the architecture. It works by building internal representations that adapt and merge knowledge across multiple domains, ensuring that the knowledge base is continuously updated. To deal with the challenge of integrating heterogeneous data, this proposal puts forward the corresponding solutions: (i) knowledge extraction, addressed via a plugin-based architecture of knowledge sensors; (ii) data integrity, tackled by an architecture designed to deal with uncertain or noisy information; (iii) scalability, this is also supported by the plugin-based architecture as only relevant knowledge to the scenario is integrated by switching-off non-relevant sensors. Also, we minimize the expert knowledge required, which may pose a bottleneck when integrating a fast-paced stream of new sources. As proof of concept, we developed a case study that deploys the architecture to integrate population census and economic data, municipal cartography, and Google Reviews to analyze the socio-economic contexts of educational institutions. The knowledge discovered enables us to answer questions that are not possible through individual sources. Thus, companies or public entities can discover patterns of behavior or relationships that would otherwise not be visible and this would allow extracting valuable information for the decision-making process.This research is supported by the University of Alicante, Spain, the Spanish Ministry of Science and Innovation, the Generalitat Valenciana, Spain, and the European Regional Development Fund (ERDF) through the following funding: At the national level, the following projects were granted: TRIVIAL (PID2021-122263OB-C22); and CORTEX (PID2021-123956OB-I00), funded by MCIN/AEI/10.13039/501100011033 and, as appropriate, by ‘‘ERDF A way of making Europe’’, by the ‘‘European Union’’ or by the ‘‘European Union NextGenerationEU/PRTR’’. At regional level, the Generalitat Valenciana (Conselleria d’Educacio, Investigacio, Cultura i Esport), Spain, granted funding for NL4DISMIS (CIPROM/2021/21)

Context-based multimedia semantics modelling and representation

The evolution of the World Wide Web, increase in processing power, and more network bandwidth have contributed to the proliferation of digital multimedia data. Since multimedia data has become a critical resource in many organisations, there is an increasing need to gain efficient access to data, in order to share, extract knowledge, and ultimately use the knowledge to inform business decisions. Existing methods for multimedia semantic understanding are limited to the computable low-level features; which raises the question of how to identify and represent the high-level semantic knowledge in multimedia resources.In order to bridge the semantic gap between multimedia low-level features and high-level human perception, this thesis seeks to identify the possible contextual dimensions in multimedia resources to help in semantic understanding and organisation. This thesis investigates the use of contextual knowledge to organise and represent the semantics of multimedia data aimed at efficient and effective multimedia content-based semantic retrieval.A mixed methods research approach incorporating both Design Science Research and Formal Methods for investigation and evaluation was adopted. A critical review of current approaches for multimedia semantic retrieval was undertaken and various shortcomings identified. The objectives for a solution were defined which led to the design, development, and formalisation of a context-based model for multimedia semantic understanding and organisation. The model relies on the identification of different contextual dimensions in multimedia resources to aggregate meaning and facilitate semantic representation, knowledge sharing and reuse. A prototype system for multimedia annotation, CONMAN was built to demonstrate aspects of the model and validate the research hypothesis, H₁.Towards providing richer and clearer semantic representation of multimedia content, the original contributions of this thesis to Information Science include: (a) a novel framework and formalised model for organising and representing the semantics of heterogeneous visual data; and (b) a novel S-Space model that is aimed at visual information semantic organisation and discovery, and forms the foundations for automatic video semantic understanding

A teachable semi-automatic web information extraction system based on evolved regular expression patterns

This thesis explores Web Information Extraction (WIE) and how it has been used in decision making and to support businesses in their daily operations. The research focuses on a WIE system based on Genetic Programming (GP) with an extensible model to enhance the automatic extractor. This uses a human as a teacher to identify and extract relevant information from the semi-structured HTML webpages. Regular expressions, which have been chosen as the pattern matching tool, are automatically generated based on the training data to provide an improved grammar and lexicon. This particularly benefits the GP system which may need to extend its lexicon in the presence of new tokens in the web pages. These tokens allow the GP method to produce new extraction patterns for new requirements

Ontologiebasierte Indexierung und Kontextualisierung multimedialer Dokumente für das persönliche Wissensmanagement

Die Verwaltung persönlicher, multimedialer Dokumente kann mit Hilfe semantischer Technologien und Ontologien intelligent und effektiv unterstützt werden. Dies setzt jedoch Verfahren voraus, die den grundlegenden Annotations- und Bearbeitungsaufwand für den Anwender minimieren und dabei eine ausreichende Datenqualität und -konsistenz sicherstellen. Im Rahmen der Dissertation wurden notwendige Mechanismen zur semi-automatischen Modellierung und Wartung semantischer Dokumentenbeschreibungen spezifiziert. Diese bildeten die Grundlage für den Entwurf einer komponentenbasierten, anwendungsunabhängigen Architektur als Basis für die Entwicklung innovativer, semantikbasierter Lösungen zur persönlichen Dokumenten- und Wissensverwaltung.Personal multimedia document management benefits from Semantic Web technologies and the application of ontologies. However, an ontology-based document management system has to meet a number of challenges regarding flexibility, soundness, and controllability of the semantic data model. The first part of the dissertation proposes necessary mechanisms for the semi-automatic modeling and maintenance of semantic document descriptions. The second part introduces a component-based, application-independent architecture which forms the basis for the development of innovative, semantic-driven solutions for personal document and information management

ScaleSem (model checking et web sémantique)

Le développement croissant des réseaux et en particulier l'Internet a considérablement développé l'écart entre les systèmes d'information hétérogènes. En faisant une analyse sur les études de l'interopérabilité des systèmes d'information hétérogènes, nous découvrons que tous les travaux dans ce domaine tendent à la résolution des problèmes de l'hétérogénéité sémantique. Le W3C (World Wide Web Consortium) propose des normes pour représenter la sémantique par l'ontologie. L'ontologie est en train de devenir un support incontournable pour l'interopérabilité des systèmes d'information et en particulier dans la sémantique. La structure de l'ontologie est une combinaison de concepts, propriétés et relations. Cette combinaison est aussi appelée un graphe sémantique. Plusieurs langages ont été développés dans le cadre du Web sémantique et la plupart de ces langages utilisent la syntaxe XML (eXtensible Meta Language). Les langages OWL (Ontology Web Language) et RDF (Resource Description Framework) sont les langages les plus importants du web sémantique, ils sont basés sur XML.Le RDF est la première norme du W3C pour l'enrichissement des ressources sur le Web avec des descriptions détaillées et il augmente la facilité de traitement automatique des ressources Web. Les descriptions peuvent être des caractéristiques des ressources, telles que l'auteur ou le contenu d'un site web. Ces descriptions sont des métadonnées. Enrichir le Web avec des métadonnées permet le développement de ce qu'on appelle le Web Sémantique. Le RDF est aussi utilisé pour représenter les graphes sémantiques correspondant à une modélisation des connaissances spécifiques. Les fichiers RDF sont généralement stockés dans une base de données relationnelle et manipulés en utilisant le langage SQL ou les langages dérivés comme SPARQL. Malheureusement, cette solution, bien adaptée pour les petits graphes RDF n'est pas bien adaptée pour les grands graphes RDF. Ces graphes évoluent rapidement et leur adaptation au changement peut faire apparaître des incohérences. Conduire l application des changements tout en maintenant la cohérence des graphes sémantiques est une tâche cruciale et coûteuse en termes de temps et de complexité. Un processus automatisé est donc essentiel. Pour ces graphes RDF de grande taille, nous suggérons une nouvelle façon en utilisant la vérification formelle Le Model checking .Le Model checking est une technique de vérification qui explore tous les états possibles du système. De cette manière, on peut montrer qu un modèle d un système donné satisfait une propriété donnée. Cette thèse apporte une nouvelle méthode de vérification et d interrogation de graphes sémantiques. Nous proposons une approche nommé ScaleSem qui consiste à transformer les graphes sémantiques en graphes compréhensibles par le model checker (l outil de vérification de la méthode Model checking). Il est nécessaire d avoir des outils logiciels permettant de réaliser la traduction d un graphe décrit dans un formalisme vers le même graphe (ou une adaptation) décrit dans un autre formalismeThe increasing development of networks and especially the Internet has greatly expanded the gap between heterogeneous information systems. In a review of studies of interoperability of heterogeneous information systems, we find that all the work in this area tends to be in solving the problems of semantic heterogeneity. The W3C (World Wide Web Consortium) standards proposed to represent the semantic ontology. Ontology is becoming an indispensable support for interoperability of information systems, and in particular the semantics. The structure of the ontology is a combination of concepts, properties and relations. This combination is also called a semantic graph. Several languages have been developed in the context of the Semantic Web. Most of these languages use syntax XML (eXtensible Meta Language). The OWL (Ontology Web Language) and RDF (Resource Description Framework) are the most important languages of the Semantic Web, and are based on XML.RDF is the first W3C standard for enriching resources on the Web with detailed descriptions, and increases the facility of automatic processing of Web resources. Descriptions may be characteristics of resources, such as the author or the content of a website. These descriptions are metadata. Enriching the Web with metadata allows the development of the so-called Semantic Web. RDF is used to represent semantic graphs corresponding to a specific knowledge modeling. RDF files are typically stored in a relational database and manipulated using SQL, or derived languages such as SPARQL. This solution is well suited for small RDF graphs, but is unfortunately not well suited for large RDF graphs. These graphs are rapidly evolving, and adapting them to change may reveal inconsistencies. Driving the implementation of changes while maintaining the consistency of a semantic graph is a crucial task, and costly in terms of time and complexity. An automated process is essential. For these large RDF graphs, we propose a new way using formal verification entitled "Model Checking".Model Checking is a verification technique that explores all possible states of the system. In this way, we can show that a model of a given system satisfies a given property. This thesis provides a new method for checking and querying semantic graphs. We propose an approach called ScaleSem which transforms semantic graphs into graphs understood by the Model Checker (The verification Tool of the Model Checking method). It is necessary to have software tools to perform the translation of a graph described in a certain formalism into the same graph (or adaptation) described in another formalismDIJON-BU Doc.électronique (212319901) / SudocSudocFranceF

Gestion dynamique d'ontologies à partir de textes par systèmes multi-agents adaptatifs

Une ontologie est une représentation structurée des connaissances d'un domaine sous la forme d'un réseau conceptuel. Les ontologies sont considérées comme un support indispensable à la communication entre agents logiciels, à l'annotation des sites Web et des ressources documentaires dans une optique de recherche sémantique de l'information. Parce que les connaissances d'un domaine sont amenées à évoluer, une ontologie doit elle aussi évoluer pour rester en cohérence avec le domaine qu'elle modélise. Actuellement, la plupart des travaux traitant de l'évolution d'ontologies se préoccupent de la vérification et du maintien de la cohérence de l'ontologie modifiée. Ces travaux n'apportent pas de solutions concrètes à l'identification de nouvelles connaissances et à leur intégration dans une ontologie. Les travaux en ingénierie d'ontologies à partir de textes quant à eux traitent ce problème d'évolution comme un problème de reconstruction d'une nouvelle ontologie. Souvent, le résultat produit est complètement différent de l'ontologie à modifier. Par ailleurs, les logiciels d'évolution spécifiques à un domaine particulier rendent impossible leur utilisation dans d'autres domaines. Cette thèse propose une solution originale basée sur les systèmes multi-agents adaptatifs (AMAS) pour faire évoluer des ontologies à partir de textes. Chaque terme et concept sont représentés par un agent qui essaie de se situer au bon endroit dans l'organisation qui n'est autre que l'ontologie. Ce travail est concrétisé par un outil nommé DYNAMO. Un besoin d'évolution est déclenché par l'ajout de nouveaux textes dans un corpus de documents. DYNAMO utilise les résultats d'un extracteur de termes et de relations lexicales ainsi qu'un AMAS, nommé DYNAMO MAS, pour proposer une ontologie modifiée à un ontographe. Ce dernier interagit avec DYNAMO MAS via une interface graphique en modifiant l'ontologie proposée (déplacement, ajout, modification de concepts, de termes et/ou de relations), produisant ainsi des contraintes auxquelles l'AMAS doit s'adapter. Cette "coévolution" entre l'AMAS et l'ontographe cesse lorsque l'ontographe juge que l'ontologie modifiée est cohérente avec le nouveau corpus.An ontology is a structured representation of domain knowledge based on a conceptual network. Ontologies are considered as an essential support for the communication between software agents, the annotation of Web sites and textual resources to carry out semantic information retieval. Because domain knowledge can evolve, an ontology must also evolve to remain consistent with the domain that it models. Currently, studies on ontologies evolution are focusing on checking and maintaining the consistency of the evolved ontology. These works do not provide concrete solutions to the identification of new knowledge and its integration into an ontology. Ontology engineering from texts considers evolution as a problem of ontology reconstruction. The result produced by this kind of software is often completely different from the initial ontology. Moreover, it is almost impossible to reuse software designed only for a particular domain. This PhD thesis proposes an original solution based on adaptive multi-agent systems (AMAS) to evolve ontologies from texts. Each term and each concept are agentified and try to find its own right place in the AMAS organization that is the ontology. This work is implemented in a software called DYNAMO. An ontology evolution requirement is triggered by the addition of new texts in a corpus of documents. DYNAMO uses the results of a term extractor and a lexical relation extractor. These results are the input data of an AMAS, called DYNAMO MAS, that evolves an ontology and proposes it to an ontologist. Then, the ontologist interacts with DYNAMO MAS via a graphical interface by modifying the proposed ontology (moving, addition, suppression of concepts, terms and / or relationships). The ontologist's actions are feedback used by the AMAS to adapt the evolved ontology. This "coevolution" process between the AMAS and the ontologist ends when the ontologist judges that the modified ontology is consistent with the new corpus