Developing Ontological Background Knowledge for Biomedicine

Biomedicine is an impressively fast developing, interdisciplinary field of research. To control the growing volumes of biomedical data, ontologies are increasingly used as common organization structures. Biomedical ontologies describe domain knowledge in a formal, computationally accessible way. They serve as controlled vocabularies and background knowledge in applications dealing with the integration, analysis and retrieval of heterogeneous types of data. The development of biomedical ontologies, however, is hampered by specific challenges. They include the lack of quality standards, resulting in very heterogeneous resources, and the decentralized development of biomedical ontologies, causing the increasing fragmentation of domain knowledge across them. In the first part of this thesis, a life cycle model for biomedical ontologies is developed, which is intended to cope with these challenges. It comprises the stages "requirements analysis", "design and implementation", "evaluation", "documentation and release" and "maintenance". For each stage, associated subtasks and activities are specified. To promote quality standards for biomedical ontology development, an emphasis is set on the evaluation stage. As part of it, comprehensive evaluation procedures are specified, which allow to assess the quality of ontologies on various levels. To tackle the issue of knowledge fragmentation, the life cycle model is extended to also cover ontology alignments. Ontology alignments specify mappings between related elements of different ontologies. By making potential overlaps and similarities between ontologies explicit, they support the integration of ontologies and help reduce the fragmentation of knowledge. In the second part of this thesis, the life cycle model for biomedical ontologies and alignments is validated by means of five case studies. As a result, they confirm that the model is effective. Four of the case studies demonstrate that it is able to support the development of useful new ontologies and alignments. The latter facilitate novel natural language processing and bioinformatics applications, and in one case constitute the basis of a task of the "BioNLP shared task 2013", an international challenge on biomedical information extraction. The fifth case study shows that the presented evaluation procedures are an effective means to check and improve the quality of ontology alignments. Hence, they support the crucial task of quality assurance of alignments, which are themselves increasingly used as reference standards in evaluations of automatic ontology alignment systems. Both, the presented life cycle model and the ontologies and alignments that have resulted from its validation improve information and knowledge management in biomedicine and thus promote biomedical research

Suporte automatizado para desenvolvimento de ontologias utilizando padrões ontológicos de domínio

A Engenharia de Ontologias tem evoluído bastante nas últimas décadas, com um número crescente de metodologias, ferramentas e aplicativos, que estão sendo propostos e experimentados na academia e na indústria. Por meio das ontologias, o conhecimento compartilhado de um domínio pode ser modelado para ser comunicado entre pessoas e sistemas automatizados. Com isso, a utilização de ontologias se torna uma importante ferramenta em diversas áreas do conhecimento para se estruturar, organizar e apoiar o compartilhamento dos conceitos que são inerentes a essas áreas. Além disso, com o uso de ontologias, a interoperabilidade entre sistemas se torna possível, devido à normatização e ao uso de padrões em sua construção. No entanto, o desenvolvimento de ontologias a partir do zero é uma tarefa difícil e complexa, uma vez que uma ontologia deve fornecer uma representação completa e coerente de uma parte específica do mundo. Assim, a reutilização é altamente recomendada em seu desenvolvimento, permitindo que as ontologias sejam construídas com base em modelos pré-existentes, levando a melhores resultados quanto a sua qualidade. Neste sentido, Padrões Ontológicos (OPs) são considerados como ferramentas interessantes para facilitar a reutilização. Recentemente, vários autores da comunidade de Engenharia de Ontologias já propuseram OPs e mecanismos para aplicá-los. No entanto, sistemas automatizados para apoiar a sua utilização na prática ainda são raros. Para preencher esta lacuna, esta dissertação propõe um editor para catálogos OPs, cujo objetivo é apoiar o gerenciamento e o reúso desses padrões. Assim, a abordagem de catálogo de OPs pode ser aplicada na construção de ontologias, com suporte automático. No desenvolvimento do editor proposto, optou-se por estender um editor de ontologias existente (o OLED) para aproveitar suas ferramentas de modelagem, verificação, transformação e validação. Também foram parcialmente implementados três catálogos de OPs específicos para os domínios de Serviço, de Processo de Software baseado na ISO e de Colaboração. Além disso, esta dissertação descreve três exemplos de utilização, um para cada um dos domínios citados, visando demonstrar a viabilidade da abordagem de construção de ontologias utilizando catálogos de OPs com o uso do editor desenvolvido, enfatizando também o benefício do reúso de OPs

Agnostic content ontology design patterns for a multi-domain ontology

This research project aims to solve the semantic heterogeneity problem. Semantic heterogeneity mimics cancer in that semantic heterogeneity unnecessarily consumes resources from its host, the enterprise, and may even affect lives. A number of authors report that semantic heterogeneity may cost a significant portion of an enterprise’s IT budget. Also, semantic heterogeneity hinders pharmaceutical and medical research by consuming valuable research funds. The RA-EKI architecture model comprises a multi-domain ontology, a cross-industry agnostic construct composed of rich axioms notably for data integration. A multi-domain ontology composed of axiomatized agnostic data model patterns would drive a cognitive data integration application system usable in any industry sector. This project’s objective is to elicit agnostic data model patterns here considered as content ontology design patterns. The first research question of this project pertains to the existence of agnostic patterns and their capacity to solve the semantic heterogeneity problem. Due to the theory-building role of this project, a qualitative research approach constitutes the appropriate manner to conduct its research. Contrary to theory testing quantitative methods that rely on well-established validation techniques to determine the reliability of the outcome of a given study, theorybuilding qualitative methods do not possess standardized techniques to ascertain the reliability of a study. The second research question inquires on a dual method theory-building approach that may demonstrate trustworthiness. The first method, a qualitative Systematic Literature Review (SLR) approach induces the sought knowledge from 69 retained publications using a practical screen. The second method, a phenomenological research protocol elicits the agnostic concepts from semi-structured interviews involving 22 senior practitioners with 21 years in average of experience in conceptualization. The SLR retains a set of 89 agnostic concepts from 2009 through 2017. The phenomenological study in turn retains 83 agnostic concepts. During the synthesis stage for both studies, data saturation was calculated for each of the retained concepts at the point where the concepts have been selected for a second time. The quantification of data saturation constitutes an element of the trustworthiness’s transferability criterion. It can be argued that this effort of establishing the trustworthiness, i.e. credibility, dependability, confirmability and transferability can be construed as extensive and this research track as promising. Data saturation for both studies has still not been reached. The assessment performed in the course of the establishment of trustworthiness of this project’s dual method qualitative research approach yields very interesting findings. Such findings include two sets of agnostic data model patterns obtained from research protocols using radically different data sources i.e. publications vs. experienced practitioners but with striking similarities. Further work is required using exactly the same protocols for each of the methods, expand the year range for the SLR and to recruit new co-researchers for the phenomenological protocol. This work will continue until these protocols do not elicit new theory material. At this point, new protocols for both methods will be designed and executed with the intent to measure theoretical saturation. For both methods, this entails in formulating new research questions that may, for example, focus on agnostic themes such as finance, infrastructure, relationships, classifications, etc. For this exploration project, the road ahead involves the design of new questionnaires for semi-structured interviews. This project will need to engage in new knowledge elicitation techniques such as focus groups. The project will definitely conduct other qualitative research methods such as research action for eliciting new knowledge and know-how from actual development and operation of an ontology-based cognitive application. Finally, a mixed methods qualitative-quantitative approach would prepare the transition toward theory testing method using hypothetico-deductive techniques

Semi-automatic Ontology Construction based on Patterns

This thesis aims to improve the ontology engineering process, by providing better semiautomatic support for constructing ontologies and introducing knowledge reuse through ontology patterns. The thesis introduces a typology of patterns, a general framework of pattern-based semi-automatic ontology construction called OntoCase, and provides a set of methods to solve some specific tasks within this framework. Experimental results indicate some benefits and drawbacks of both ontology patterns, in general, and semi-automatic ontology engineering using patterns, the OntoCase framework, in particular. The general setting of this thesis is the field of information logistics, which focuses on how to provide the right information at the right moment in time to the right person or organisation, sent through the right medium. The thesis focuses on constructing enterprise ontologies to be used for structuring and retrieving information related to a certain enterprise. This means that the ontologies are quite 'light weight' in terms of logical complexity and expressiveness. Applying ontology content design patterns within semi-automatic ontology construction, i.e. ontology learning, is a novel approach. The main contributions of this thesis are a typology of patterns together with a pattern catalogue, an overall framework for semi-automatic patternbased ontology construction, specific methods for solving partial problems within this framework, and evaluation results showing the characteristics of ontologies constructed semiautomatically based on patterns. Results show that it is possible to improve the results of typical existing ontology learning methods by selecting and reusing patterns. OntoCase is able to introduce a general top-structure to the ontologies, and by exploiting background knowledge, the ontology is given a richer structure than when patterns are not applied