Ontologies play key roles in many applications today. Therefore, whether using a newly-specified ontology or an existing ontology for use in its target application, it is important to determine the suitability of an ontology to the application at hand. This need is addressed by carrying out ontology evaluation, which determines qualities of an ontology using methodologies, criteria or measures. However, for addressing the ontology requirements from a given application, it is necessary to determine what the appropriate set of criteria and measures are. In this thesis, we propose a Requirements-Oriented Methodology for Evaluating Ontologies (ROMEO). ROMEO outlines a methodology for determining appropriate methods for ontology evaluation that incorporates a suite of existing ontology evaluation criteria and measures. ROMEO helps ontology engineers to determine relevant ontology evaluation measures for a given set of ontology requirements by linking these requirements to existing ontology evaluation measures through a set of questions. There are three main parts to ROMEO. First, ontology requirements are elicited from a given application and form the basis for an appropriate evaluation of ontologies. Second, appropriate questions are mapped to each ontology requirement. Third, relevant ontology evaluation measures are mapped to each of those questions. From the ontology requirements of an application, ROMEO is used to determine appropriate methods for ontology evaluation by mapping applicable questions to the requirements and mapping those questions to appropriate measures. In this thesis, we perform the ROMEO methodology to obtain appropriate ontology evaluation methods for ontology-driven applications through case studies of Lonely Planet and Wikipedia. Since the mappings determined by ROMEO are dependent on the analysis of the ontology engineer, the validation of these mappings is needed. As such, in addition to proposing the ROMEO methodology, a method for the empirical validation of ROMEO mappings is proposed in this thesis. We report on two empirical validation experiments that are carried out in controlled environments to examine the performance of the ontologies over a set of tasks. These tasks vary and are used to compare the performance of a set of ontologies in the respective experimental environment. The ontologies used vary on a specific ontology quality or measure being examined. Empirical validation experiments are conducted for two mappings between questions and their associated measures, which are drawn from case studies of Lonely Planet and Wikipedia. These validation experiments focus on mappings between questions and their measures. Furthermore, as these mappings are application-independent, they may be reusable in subsequent applications of the ROMEO methodology. Using a ROMEO mapping from the Lonely Planet case study, we validate a mapping of a coverage question to the F-measure. The validation experiment carried out for this mapping was inconclusive, thus requiring further analysis. Using a ROMEO mapping from the Wikipedia case study, we carry out a separate validation experiment examining a mapping between an intersectedness question and the tangledness measure. The results from this experiment showed the mapping to be valid. For future work, we propose additional validation experiments for mappings that have been identified between questions and measures.

This research contributes to the field of ontology-based semantic matching techniques and also to the field of Instant Messaging (IM) based enhanced presence. It aims to achieve a mutually beneficial development of two fields through interactions in their use of data and their functionality. With respect to semantic matching this research has developed a collaborative and self-evolutionary approach based on user involvement in order to overcome disadvantages of traditional ontology-based approaches. At the same time, enhanced semantic matching algorithms were also explored and developed to achieve better performance when searching and querying through the ontology. In order to realize this automatic, dynamic and collaborative approach, a Jabber-based IM system was built to support its development with specific data and to evaluate its performance. In the prototype of the system, Computer Science area is selected to be the domain of the ontology in order to demonstrate the practicability of the new approach. With respect to enhanced presence an efficient semantic-based contacts search engine which can feature context-based search ranking is provided to support academic researchers. It is especially designed to help new academic researchers to find potential contacts who share a common research interest. It enriches the IM system's presence information, and helps the user to pick the most suitable contacts and conveniently organize meetings or co-operating with others. Consequently, this research improves the efficiency of users' academic researching, and extends users' relationship radius during their academic research careers. The contributions are particularly highlighted by the comprehensive support during the academic user's self-educational process.

Ontologies are machine processable artifacts and the core structures of the Semantic Web. OWL (Web Ontology Language) is a W3C Recommendation language for developing ontologies; it is based on Description Logics, allowing for precise knowledge representation and sound and complete automated reasoning over the collection of axioms in an OWL document. Although ontologies are useful for sharing terminologies, their design and reuse are difficult and time consuming processes. Despite the efforts of the community towards the development of OWL ontologies, there is a lack of methods and tools for reusing and inspecting ontologies, i.e., reverse engineering methods. This thesis focuses on the area by investigating the detection of regularities in ontologies, for the purpose of abstracting sets of axioms into patterns that can be verified and reused. Its main contribution is the Regularity Inspector for Ontologies (RIO) framework, which implements methods to find syntactic regularities (repetitive structures in the asserted axioms) and semantic regularities (repetitive structures in the entailments) in an ontology. Regularity detection is achieved through the use of cluster analysis for detecting similarities in sets of axioms. This thesis provides experimental evidence for the effectiveness of regularity analysis for the inspection of patterns, and the discovery of modeling irregularities (often modelling errors) during quality assurance for real, large ontologies. In particular, empirical analysis showed that RIO could successfully detect regularities in ontologies, revealing the patterns adopted by the developers. It can be also used to trace pattern deviations as part of checking conformance to an intended design template during quality assurance of an ontology. This work has been motivated by the existence of pattern based systematic development methodologies and the lack of methods for discovering patterns in existing ontologies --- the natural complement of these pattern based development methodologies.

Semantically-enabled applications, such as ontology-based search and data integration, take into account the semantics of the input data in their algorithms. Such applications often use ontologies, which model the application domains in question, as well as alignments, which provide information about the relationships between the terms in the different ontologies. The quality and reliability of the results of such applications depend directly on the correctness and completeness of the ontologies and alignments they utilize. Traditionally, ontology debugging discovers defects in ontologies and alignments and provides means for improving their correctness and completeness, while ontology alignment establishes the relationships between the terms in the different ontologies, thus addressing completeness of alignments. This thesis focuses on the integration of ontology alignment and debugging for taxonomy networks which are formed by taxonomies, the most widely used kind of ontologies, connected through alignments. The contributions of this thesis include the following. To the best of our knowledge, we have developed the first approach and framework that integrate ontology alignment and debugging, and allow debugging of modelling defects both in the structure of the taxonomies as well as in their alignments. As debugging modelling defects requires domain knowledge, we have developed algorithms that employ the domain knowledge intrinsic to the network to detect and repair modelling defects. Further, a system has been implemented and several experiments with real-world ontologies have been performed in order to demonstrate the advantages of our integrated ontology alignment and debugging approach. For instance, in one of the experiments with the well-known ontologies and alignment from the Anatomy track in Ontology Alignment Evaluation Initiative 2010, 203 modelling defects (concerning incomplete and incorrect information) were discovered and repaired.

The expansion of Semantic Web Services is restricted by traditional ontology engineering methods. Manual ontology development is time consuming, expensive and a resource exhaustive task. Consequently, it is important to support ontology engineers by automating the ontology acquisition process to help deliver the Semantic Web vision. Existing Web Services offer an affluent source of domain knowledge for ontology engineers. Ontology learning can be seen as a plug-in in the Web Service ontology development process, which can be used by ontology engineers to develop and maintain an ontology that evolves with current Web Services. Supporting the domain engineer with an automated tool whilst building an ontological domain model, serves the purpose of reducing time and effort in acquiring the domain concepts and relations from Web Service artefacts, whilst effectively speeding up the adoption of Semantic Web Services, thereby allowing current Web Services to accomplish their full potential. With that in mind, a Service Ontology Learning Framework (SOLF) is developed and applied to a real set of Web Services. The research contributes a rigorous method that effectively extracts domain concepts, and relations between these concepts, from Web Services and automatically builds the domain ontology. The method applies pattern-based information extraction techniques to automatically learn domain concepts and relations between those concepts. The framework is automated via building a tool that implements the techniques. Applying the SOLF and the tool on different sets of services results in an automatically built domain ontology model that represents semantic knowledge in the underlying domain. The framework effectiveness, in extracting domain concepts and relations, is evaluated by its appliance on varying sets of commercial Web Services including the financial domain. The standard evaluation metrics, precision and recall, are employed to determine both the accuracy and coverage of the learned ontology models. Both the lexical and structural dimensions of the models are evaluated thoroughly. The evaluation results are encouraging, providing concrete outcomes in an area that is little researched.

A formalização e o compartilhamento do conhecimento tem incentivado cada vez mais o uso de ontologias em diversas áreas da computação. Na Engenharia de Software, por exemplo, elas são usadas em diferentes fases do ciclo de vida do software. Especificamente no desenvolvimento de software a ontologia pode ser considerada como um artefato de software que atua na formalização do conhecimento e requisitos, na geração automática de código, na integração contínua e na transformação de dados em conhecimento. No entanto, poucos estudos abordam esses fatores de maneira sistematizada na construção do software baseado em ontologia, ao associar os conceitos da Engenharia de Software à Engenharia de Ontologias. Além disso, as abordagens atuais não inserem princípios ágeis em suas definições. Portanto, este trabalho tem por objetivo definir um processo de desenvolvimento considerando os princípios e valores ágeis para o desenvolvimento de software baseado em ontologia. No processo, denominado OntoSoft, fases, atividades, tarefas, papeis e modelos de artefatos foram definidos de maneira detalhada para guiar as equipes de desenvolvimento. Ademais, foram especificados três cenários de desenvolvimento considerando a complexidade do software a ser desenvolvido, a fim de evidenciar possibilidades distintas na sequência das atividades durante o fluxo de desenvolvimento do software baseado em ontologia. Com base nos estudos de caso conduzidos em diferentes cenários de desenvolvimento, os resultados sugerem que o processo OntoSoft contribui positivamente na produção dos artefatos do software baseado em ontologia, colaborando para a efetividade e produtividade da equipe. / Formalization and knowledge sharing have increasingly encouraged the use of ontologies in several areas of computing. In Software Engineering, for example, they have been used in different phases of the software life cycle. Specifically in software development, an ontology can be considered as a software artifact, which acts in the formalization of knowledge and requirements, automatic generation of code, continuous integration and data transformation into knowledge. However, few studies deal with these factors in a systematized way for the development of ontology based software, regarding to associating Software Engineering and Ontology Engineering concepts. In addition, current approaches do not address agile principles in their definitions. In this sense, this work aims to define a development process concerning the principles and agile values for ontology based software development. In the process, called OntoSoft, phases, activities, tasks, roles, and artifact models were defined in detail to guide development teams. In addition, three development scenarios were specified considering the complexity of the software to be developed, in order to demonstrate distinct possibilities of development flow of the ontology based software. Based on case studies conducted in different development environments, the results suggest that the OntoSoft process contributes positively to the development of ontology based software artifacts, contributing to the effectiveness and productivity of the team.

<p>This thesis contributes to the research field information logistics. Information logistics aims at improving information flow and at reducing information overload by providing the right information, in the right context, at the right time, at the right place through the right channel.</p><p>Ontologies are expected to contribute to reduced information overload and solving information supply problems. An ontology is created to form some kind of shared understanding for the involved stakeholders in the domain at hand. By using this semantic structure you can further build applications that use the ontology and support the employee by providing only the most important information for this person.</p><p>During the last years, there has been an increasing number of successful cases in which industrial applications successfully use ontologies. Most of these cases however, stem from large enterprises or IT-intensive small or medium-sized enterprises (SME). The current ontology development methodologies are not tailored for SME and their specific demands and preferences, such as that SME prefer mature technologies, and show a clear preference for to a large extent standardised solutions. The author proposes a new ontology development methodology, taking the specific characteristics of SME into consideration. This methodology was tested in an application case, which resulted in a number of concrete improvement ideas, but also the conclusion that further specialisation of the methodology was needed, for example for a specific usage area or domain. In order to find out in which direction to specify the methodology a survey was performed among SME in the region of Jönköping.</p><p>The main conclusion from the survey is that ontologies can be expected to be useful for SME mainly in the area of product configuration and variability modelling. Another area of interest is document management for supporting project work. The area of information search and retrieval can also be seen as a possible application field, as many of the respondents of the survey spend much time finding and saving information.</p>

<p>Due to the explosion of the amount of biomedical data, knowledge and tools that are often publicly available over the Web, a number of difficulties are experienced by biomedical researchers. For instance, it is difficult to find, retrieve and integrate information that is relevant to their research tasks. Ontologies and the vision of a Semantic Web for life sciences alleviate these difficulties. In recent years many biomedical ontologies have been developed and many of these ontologies contain overlapping information. To be able to use multiple ontologies they have to be aligned or merged. A number of systems have been developed for aligning and merging ontologies and various alignment strategies are used in these systems. However, there are no general methods to support building such tools, and there exist very few evaluations of these strategies. In this thesis we give an overview of the existing systems. We propose a general framework for aligning and merging ontologies. Most existing systems can be seen as instantiations of this framework. Further, we develop SAMBO (System for Aligning and Merging Biomedical Ontologies) according to this framework. We implement different alignment strategies and their combinations, and evaluate them in terms of quality and processing time within SAMBO. We also compare SAMBO with two other systems. The work in this thesis is a first step towards a general framework that can be used for comparative evaluations of alignment strategies and their combinations.</p> / Report code: LiU-Tek-Lic-2006:6.

A formalização e o compartilhamento do conhecimento tem incentivado cada vez mais o uso de ontologias em diversas áreas da computação. Na Engenharia de Software, por exemplo, elas são usadas em diferentes fases do ciclo de vida do software. Especificamente no desenvolvimento de software a ontologia pode ser considerada como um artefato de software que atua na formalização do conhecimento e requisitos, na geração automática de código, na integração contínua e na transformação de dados em conhecimento. No entanto, poucos estudos abordam esses fatores de maneira sistematizada na construção do software baseado em ontologia, ao associar os conceitos da Engenharia de Software à Engenharia de Ontologias. Além disso, as abordagens atuais não inserem princípios ágeis em suas definições. Portanto, este trabalho tem por objetivo definir um processo de desenvolvimento considerando os princípios e valores ágeis para o desenvolvimento de software baseado em ontologia. No processo, denominado OntoSoft, fases, atividades, tarefas, papeis e modelos de artefatos foram definidos de maneira detalhada para guiar as equipes de desenvolvimento. Ademais, foram especificados três cenários de desenvolvimento considerando a complexidade do software a ser desenvolvido, a fim de evidenciar possibilidades distintas na sequência das atividades durante o fluxo de desenvolvimento do software baseado em ontologia. Com base nos estudos de caso conduzidos em diferentes cenários de desenvolvimento, os resultados sugerem que o processo OntoSoft contribui positivamente na produção dos artefatos do software baseado em ontologia, colaborando para a efetividade e produtividade da equipe. / Formalization and knowledge sharing have increasingly encouraged the use of ontologies in several areas of computing. In Software Engineering, for example, they have been used in different phases of the software life cycle. Specifically in software development, an ontology can be considered as a software artifact, which acts in the formalization of knowledge and requirements, automatic generation of code, continuous integration and data transformation into knowledge. However, few studies deal with these factors in a systematized way for the development of ontology based software, regarding to associating Software Engineering and Ontology Engineering concepts. In addition, current approaches do not address agile principles in their definitions. In this sense, this work aims to define a development process concerning the principles and agile values for ontology based software development. In the process, called OntoSoft, phases, activities, tasks, roles, and artifact models were defined in detail to guide development teams. In addition, three development scenarios were specified considering the complexity of the software to be developed, in order to demonstrate distinct possibilities of development flow of the ontology based software. Based on case studies conducted in different development environments, the results suggest that the OntoSoft process contributes positively to the development of ontology based software artifacts, contributing to the effectiveness and productivity of the team.

SAMBO is a system to assist users for alignment and merging of two ontologies (i.e. to find inter-ontology relationship). The user performs an alignment process with the help of mapping suggestions. The objective of the thesis work is to extend the existing system with new components; multiple sessions, integration of an ontology alignment strategy, recommendation system, integration of a system that can use results from previous sessions, and integration of partial reference alignment (PRA) that can be used to filter mapping suggestions. Most of the theoretical work existed, but it was important to study and implement, how these components can be integrated in the system, and how they can work together.