Made available in DSpace on 2014-06-12T15:52:23Z (GMT). No. of bitstreams: 1 license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2009 / Devido aos problemas relacinados com o gerenciamento e o raciocínio em grandes ontologias, um forte interesse e uma conseqüente investigação ativa em modularização de ontologias vem emergindo na comunidade científica relacionada à Web Semântica. Uma vez que muitas ontologias são preferivelmente grandes artefatos, para uma adoção em grande escala das mesmas, por exemplo, na Web Semântica, é necessário permitir que desenvolvedores de ontologias possam incluir apenas as entidades e axiomas que são relevantes para a aplicação que estão a desenvolver. Além da reutilização, o uso de modularização de ontologias é útil para muitas outras tarefas, incluindo suporte a queries, raciocínio distribuído, desenvolvimento em grande escala e manutenção de ontologias. Alguns abordagens para modularização têm sido propostas, no entanto nemhuma delas dispõe de uma ferramenta flexível que permita não só definição de módulos, mas também outros tipos de tarefas, como análise sintática e semântica, biblioteca de módulos e uma poderosa ferramenta para seleção de entidades. Esta dissertação propõe um conjunto de ferramentas, chamado ModOnto, para cumprir esses requisitos. Incorpora uma abordagem para modularização de ontologias que herda alguns dos princípios de Engenharia de Software Orientada para Objeto, que são o encapsulamento e o ocultamento de informação

Knowledge-driven problem solving demands 'complete' knowledge about the domain and its interpretation under different contexts. Knowledge Morphing aims at a context-driven integration of heterogeneous knowledge sources--in order to provide a comprehensive and networked view of all knowledge about a domain-specific problem, pertaining to the context at hand. In this PhD thesis, we have proposed a Semantic Web based framework, K-MORPH, for Knowledge Morphing via Reconciliation of Contextualized Sub-ontologies. In order to realize our K-MORPH framework, we have developed: (i) a sub-ontology extraction method for generating contextualized sub-ontologies from the source ontologies pertinent to the problem-context at hand; (ii) two ontology matching approaches: triple-based ontology matching (TOM) and proof-based ontology matching (POM) for finding both atomic and complex correspondences between two extracted contextualized sub-ontologies; and (iii) our approach for resolving inconsistencies in ontologies by generating minimal inconsistent resolve candidates (MIRCs), where removing any of the MIRCs from the inconsistent ontology results in a maximal consistent sub-ontology. Thus, K-MORPH performs knowledge morphing among ontology-modelled knowledge sources and generates a context-sensitive and comprehensive knowledge-base pertinent to the problem at hand by (a) extracting problem-specific knowledge components from ontology-modelled knowledge sources using our sub-ontology extraction method; (b) aligning and merging the extracted knowledge components using our matching approaches; and (c) repairing inconsistencies in the morphed knowledge by applying our approach for detecting and resolving inconsistencies. We demonstrated the application of our K-MORPH framework in the healthcare domain, where K-MORPH generated a merged ontology for providing a comprehensive therapeutic knowledge-base for Urinary Tract Infections (UTI) by first (i) extracting 20 contextualized sub-ontologies from various UTI ontologies of different healthcare institutions, (ii) aligning and merging the extracted UTI sub-ontologies, and (iii) detecting and resolving inconsistencies in the merged UTI ontology.

The study of human affective states and their influences has been a research interest in psychology for some time. Fortunately, the presence of an affective computing paradigm allows us to use theories and findings from the discipline of psychology in the representation and development of human affective applications. However, because of the complexity of the subject, it is possible to misunderstand concepts that are shared via human and/or computer communications. With the appearance of technological innovations in our lives, for instance the SemanticWeb and the Web Ontology Language (OWL), there is a stronger need for computers to better understand human affective states and their influences. The use of an ontology can be beneficial in order to represent human affective states and their influences in a machine-understandable format. Truly, ontologies provide powerful tools to make sense of data. Our thesis proposes HASIO, a Human Affective States and their Influences Ontology, designed based on existing psychological theories. HASIO was developed to represent the knowledge that is necessary to model affective states and their influences in a computerized format. It describes the human affective states (Emotion, Mood and Sentiment) and their influences (Personality, Need and Subjective well-being) and conceptualizes their models and recognition methods. HASIO also represents the relationships between affective states and the factors that influence them. We surveyed and analyzed existing ontologies regarding human affective states and their influences to realize the significance and profit of developing our proposed ontology (HASIO). We follow the Methontology approach, a comprehensive engineering methodology for ontology building, to design and build HASIO. An important aspect in determining the ontology scope is Competency Questions (CQs). We configure HASIO CQs by analyzing the resources from psychology theories, available lexicons and existing ontologies. In this thesis, we present the development, modularization and evaluation of HASIO. HASIO can profit from the modularization process by dividing the whole ontology in self-contained modules that are easy to reuse and maintain. The ontology is evaluated through Question Answering system (HASIOQA), a task-based evaluation system, for validation. We design and develop a natural language interface system for this purpose. Moreover, the proposed ontology was evaluated through the Ontology Pitfall Scanner for verification and correctness against several criteria. Furthermore, HASIO was used in sentiment analysis on diffrent Twitter dataset. We designed and developed a tweet polarity calculation algorithm. Additionally, we compare our ontology result with machine learning technique. We demonstrate and highlight the advantage of using ontology in sentiment analysis.

Le but essentiel de la thèse est de développer une ontologie juridique bien fondée pour l'utiliser dans le raisonnement à base des règles. Pour cela, une approche middle-out, collaborative et modulaire est proposée ou des ontologies fondationnelles et core ont été réutilisées pour simplifier le développement de l'ontologie. L’ontologie résultante est adoptée dans une approche homogène a base des ontologies pour formaliser la liste des règles juridiques du code pénal en utilisant le langage logique SWRL. / This thesis analyses the problem of building well-founded domain ontologies for reasoning and decision support purposes. Specifically, it discusses the building of legal ontologies for rule-based reasoning. In fact, building well-founded legal domain ontologies is considered as a difficult and complex process due to the complexity of the legal domain and the lack of methodologies. For this purpose, a novel middle-out approach called MIROCL is proposed. MIROCL tends to enhance the building process of well-founded domain ontologies by incorporating several support processes such as reuse, modularization, integration and learning. MIROCL is a novel modular middle-out approach for building well-founded domain ontologies. By applying the modularization process, a multi-layered modular architecture of the ontology is outlined. Thus, the intended ontology will be composed of four modules located at different abstraction levels. These modules are, from the most abstract to the most specific, UOM(Upper Ontology Module), COM(Core Ontology Module), DOM(Domain Ontology Module) and DSOM(Domain-Specific Ontology Module). The middle-out strategy is composed of two complementary strategies: top-down and bottom-up. The top-down tends to apply ODCM (Ontology-Driven Conceptual Modeling) and ontology reuse starting from the most abstract categories for building UOM and COM. Meanwhile, the bottom-up starts from textual resources, by applying ontology learning process, in order to extract the most specific categories for building DOM and DSOM. After building the different modules, an integration process is performed for composing the whole ontology. The MIROCL approach is applied in the criminal domain for modeling legal norms. A well-founded legal domain ontology called CriMOnto (Criminal Modular Ontology) is obtained. Therefore, CriMOnto has been used for modeling the procedural aspect of the legal norms by the integration with a logic rule language (SWRL). Finally, an hybrid approach is applied for building a rule-based system called CORBS. This system is grounded on CriMOnto and the set of formalized rules.