There has been an increasing interest in Semantic Web services (SWS) as a proposed solution to facilitate automatic discovery, composition and deployment of existing syntactic Web services. Successful implementation and wider adoption of SWS by research and industry are, however, profoundly based on the existence of effective and easy to use methods for service semantic description. Unfortunately, Web service semantic annotation is currently performed by manual means. Manual annotation is a difficult, error-prone and time-consuming task and few approaches exist aiming to semi-automate that task. Existing approaches are difficult to use since they require ontology building. Moreover, these approaches employ ineffective matching methods and suffer from the Low Percentage Problem. The latter problem happens when a small number of service elements - in comparison to the total number of elements – are annotated in a given service. This research addresses the Web services annotation problem by developing a semi-automatic annotation approach that allows SWS developers to effectively and easily annotate their syntactic services. The proposed approach does not require application ontologies to model service semantics. Instead, a standard query template is used: This template is filled with data and semantics extracted from WSDL files in order to produce query instances. The input of the annotation approach is the WSDL file of a candidate service and a set of ontologies. The output is an annotated WSDL file. The proposed approach is composed of five phases: (1) Concept extraction; (2) concept filtering and query filling; (3) query execution; (4) results assessment; and (5) SAWSDL annotation. The query execution engine makes use of name-based and structural matching techniques. The name-based matching is carried out by CN-Match which is a novel matching method and tool that is developed and evaluated in this research. The proposed annotation approach is evaluated using a set of existing Web services and ontologies. Precision (P), Recall (R), F-Measure (F) and Percentage of annotated elements are used as evaluation metrics. The evaluation reveals that the proposed approach is effective since - in relation to manual results - accurate and almost complete annotation results are obtained. In addition, high percentage of annotated elements is achieved using the proposed approach because it makes use of effective ontology extension mechanisms.

Na área de emparelhamento de ontologias, são utilizadas algumas métricas para avaliar os alinhamentos produzidos. As métricas baseadas em alinhamento têm como princípio básico confrontar um alinhamento proposto com um alinhamento de referência. Algumas destas métricas, entretanto, não têm alcançado êxito suficiente porque (i) não conseguem discriminar sempre entre um alinhamento totalmente errado e um quase correto; e (ii) não conseguem estimar o esforço do usuário para refinar o alinhamento resultante. Este trabalho tem como objetivo apresentar uma nova abordagem para avaliar os alinhamentos de ontologias. A nossa abordagem apresenta uma métrica na qual utilizamos as próprias consultas normalmente já realizadas nas ontologias originais para julgar a qualidade do alinhamento proposto. Apresentamos também alguns resultados satisfatórios de nossa abordagem em relação às outras métricas já existentes e largamente utilizadas. / In the ontology matching field, different metrics are used to evaluate the resulting alignments. Metrics based on alignment adopt the basic principle of verifying a proposed alignment against a reference alignment. Some of these metrics do not achieve good results because (i) they cannot always distinguish between a totally wrong alignment and one which is almost correct; and (ii) they cannot estimate the effort for the user to refine the resulting alignment. This work aims to present a new approach to evaluate ontology alignments. Our approach presents a measure that uses the usual queries in the original ontologies to assess the quality of the proposed alignment. We also present some satisfactory results of our approach with regard to widely used metrics.

Cette thèse étudie l'intérêt du contexte pour améliorer l'interopérabilité entre ontologies hétérogènes, d'une manière qui permette leur évolution indépendante. Lors de collaborations, les organisations échangent leurs données, qui sont décrites par des concepts définis dans des ontologies. L'objectif est d'obtenir un service d'évaluation de tels concepts, basé sur le contexte.<br />Nous proposons une méthodologie pour déterminer, modeler et utiliser le contexte. En l'appliquant, nous découvrons trois usages du contexte qui contribuent à améliorer la réconciliation d'ontologies : Nous proposons de désambiguïser les sens pragmatiques possibles des concepts en comparant les "perspectives" avec lesquelles les concepts ont été développés ; de personnaliser en considérant le contexte des agents, constitué d'une sélection pertinente parmi les domaines et tâches de l'organisation ; d'évaluer la pertinence des données associées au concept pour la tâche qui a suscité le besoin en interopérabilité.

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.

Made available in DSpace on 2014-06-12T15:49:23Z (GMT). No. of bitstreams: 1 license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2009 / Faculdade de Amparo à Ciência e Tecnologia do Estado de Pernambuco / Os Sistemas P2P de Gerenciamento de Dados (PDMS) são aplicações P2P avançadas que permitem aos usuários consultar, de forma transparente, várias fontes de dados distribuídas, heterogêneas e autônomas. Cada peer representa uma fonte de dados e exporta seu esquema de dados completo ou apenas uma parte dele. Tal esquema, denominado esquema exportado, representa os dados a serem compartilhados com outros peers no sistema e é comumente descrito por uma ontologia. Os dois aspectos mais estudados sobre gerenciamento de dados em PDMS estão relacionados com mapeamentos entre esquemas e processamento de consultas. Estes aspectos podem ser melhorados se os peers estiverem eficientemente dispostos na rede overlay de acordo com uma abordagem baseada em semântica. Nesse contexto, a noção de comunidade semântica de peers é bastante importante visto que permite aproximar logicamente peers com interesses comuns sobre um tópico específico. Entretanto, devido ao comportamento dinâmico dos peers, a criação e manutenção de comunidades semânticas é um aspecto desafiador no estágio atual de desenvolvimento dos PDMS. O objetivo principal desta tese é propor um processo baseado em semântica para agrupar, de modo incremental, peers semanticamente similares que compõem comunidades em um PDMS. Nesse processo, os peers são agrupados de acordo com o respectivo esquema exportado (uma ontologia) e processos de gerenciamento de ontologias (por exemplo, matching e sumarização) são utilizados para auxiliar a conexão dos peers. Uma arquitetura de PDMS é proposta para facilitar a organização semântica dos peers na rede overlay. Para obter a similaridade semântica entre duas ontologias de peers, propomos uma medida de similaridade global como saída de um processo de ontology matching. Para otimizar o matching entre ontologias, um processo automático para sumarização de ontologias também é proposto. Um simulador foi desenvolvido de acordo com a arquitetura do PDMS. Os processos de gerenciamento de ontologias propostos também foram desenvolvidos e incluídos no simulador. Experimentações de cada processo no contexto do PDMS assim como os resultados obtidos a partir dos experimentos são apresentadas

Na área de emparelhamento de ontologias, são utilizadas algumas métricas para avaliar os alinhamentos produzidos. As métricas baseadas em alinhamento têm como princípio básico confrontar um alinhamento proposto com um alinhamento de referência. Algumas destas métricas, entretanto, não têm alcançado êxito suficiente porque (i) não conseguem discriminar sempre entre um alinhamento totalmente errado e um quase correto; e (ii) não conseguem estimar o esforço do usuário para refinar o alinhamento resultante. Este trabalho tem como objetivo apresentar uma nova abordagem para avaliar os alinhamentos de ontologias. A nossa abordagem apresenta uma métrica na qual utilizamos as próprias consultas normalmente já realizadas nas ontologias originais para julgar a qualidade do alinhamento proposto. Apresentamos também alguns resultados satisfatórios de nossa abordagem em relação às outras métricas já existentes e largamente utilizadas. / In the ontology matching field, different metrics are used to evaluate the resulting alignments. Metrics based on alignment adopt the basic principle of verifying a proposed alignment against a reference alignment. Some of these metrics do not achieve good results because (i) they cannot always distinguish between a totally wrong alignment and one which is almost correct; and (ii) they cannot estimate the effort for the user to refine the resulting alignment. This work aims to present a new approach to evaluate ontology alignments. Our approach presents a measure that uses the usual queries in the original ontologies to assess the quality of the proposed alignment. We also present some satisfactory results of our approach with regard to widely used metrics.

Diese Masterarbeit befasst sich mit Verfahren für die Reparatur von Ontologie-Mappings in den Lebenswissenschaften. Ontologien sind eine Art der Wissensrepräsentation, die für die Beschreibung und Strukturierung von Daten verwendet werden. Besonders in den Lebenswissenschaften werden Ontologien verwendet, um biologische Objekte zu beschreiben. Das Ontologie-Matching ist ein essentieller Prozess im Bereich der Datenintegration. Aufgrund der Bedeutung des Ontologie-Matching existieren auf diesem Forschungsgebiet eine immense Anzahl an Verfahren, die sich mit dieser Thematik befassen. Jedoch können die generierten Mappings eine Vielzahl von logischen Konflikten aufweisen. Aufgrund dieser Problematik sind Verfahren notwendig, die für ein gegebenes Ontologie-Mapping die Konflikte reduziert. Da die Ontologien potentiell sehr groß sein können, müssen die Reparaturverfahren effizient und effektiv realisiert werden. Diese Arbeit fokussiert sich auf die existierenden Reparaturverfahren und die Konzeption und Realisierung eines eigenen Verfahrens. Alle Verfahren werden bzgl. der Effektivität und Effizienz für sehr große Ontologien im Bereich der Lebenswissenschaften evaluiert.

Ontologies are heavily developed and used in life sciences and undergo continuous changes. However, the evolution of life science ontologies and references to them (e.g., annotations) is not well understood and has received little attention so far. We therefore propose a generic framework for analyzing both the evolution of ontologies and the evolution of ontology-related mappings, in particular annotations referring to ontologies and similarity (match) mappings between ontologies. We use our framework for an extensive comparative evaluation of evolution measures for 16 life science ontologies. Moreover, we analyze the evolution of annotation mappings and ontology mappings for the Gene Ontology.

Ontologies are heavily used in life sciences so that there is increasing value to match different ontologies in order to determine related conceptual categories. We propose a simple yet powerful methodology for instance-based ontology matching which utilizes the associations between molecular-biological objects and ontologies. The approach can build on many existing ontology associations for instance objects like sequences and proteins and thus makes heavy use of available domain knowledge. Furthermore, the approach is flexible and extensible since each instance source with associations to the ontologies of interest can contribute to the ontology mapping. We study several approaches to determine the instance-based similarity of ontology categories. We perform an extensive experimental evaluation to use protein associations for different species to match between subontologies of the Gene Ontology and OMIM. We also provide a comparison with metadata-based ontology matching.

Matching and integrating ontologies has been a desirable technique in areas such as data fusion, knowledge integration, the Semantic Web and the development of advanced services in distributed system. Unfortunately, the heterogeneities of ontologies cause big obstacles in the development of this technique. This licentiate thesis describes an approach to tackle the problem of ontology integration using description logics and production rules, both on a syntactic level and on a semantic level. Concepts in ontologies are matched and integrated to generate ontology intersections. Context is extracted and rules for handling heterogeneous ontology reasoning with contexts are developed. Ontologies are integrated by two processes. The first integration is to generate an ontology intersection from two OWL ontologies. The result is an ontology intersection, which is an independent ontology containing non-contradictory assertions based on the original ontologies. The second integration is carried out by rules that extract context, such as ontology content and ontology description data, e.g. time and ontology creator. The integration is designed for conceptual ontology integration. The information of instances isn't considered, neither in the integrating process nor in the integrating results. An ontology reasoner is used in the integration process for non-violation check of two OWL ontologies and a rule engine for handling conflicts according to production rules. The ontology reasoner checks the satisfiability of concepts with the help of anchors, i.e. synonyms and string-identical entities; production rules are applied to integrate ontologies, with the constraint that the original ontologies should not be violated. The second integration process is carried out with production rules with context data of the ontologies. Ontology reasoning, in a repository, is conducted within the boundary of each ontology. Nonetheless, with context rules, reasoning is carried out across ontologies. The contents of an ontology provide context for its defined entities and are extracted to provide context with the help of an ontology reasoner. Metadata of ontologies are criteria that are useful for describing ontologies. Rules using context, also called context rules, are developed and in-built in the repository. New rules can also be added. The scientific contribution of the thesis is the suggested approach applying semantic based techniques to provide a complementary method for ontology matching and integrating semantically. With the illustration of the ontology integration process and the context rules and a few manually integrated ontology results, the approach shows the potential to help to develop advanced knowledge-based services. / <p>QC 20130201</p>