Metarel, an ontology facilitating advanced querying of biomedical knowledge

Knowledge management has become indispensible in the Life Sciences for integrating and querying the enormous amounts of detailed knowledge about genes, organisms, diseases, drugs, cells, etc. Such detailed knowledge is continuously generated in bioinformatics via both hardware (e.g. raw data dumps from micro‐arrays) and software (e.g. computational analysis of data). Well‐known frameworks for managing knowledge are relational databases and spreadsheets. The doctoral dissertation describes knowledge management in two more recently‐investigated frameworks: ontologies and the Semantic Web. Knowledge statements like ‘lions live in Africa’ and ‘genes are located in a cell nucleus’ are managed with the use of URIs, logics and the ontological distinction between instances and classes. Both theory and practice are described. Metarel, the core subject of the dissertation, is an ontology describing relations that can bridge the mismatch between network‐based relations that appeal to internet browsing and logic‐based relations that are formally expressed in Description Logic. Another important subject of the dissertation is BioGateway, which is a knowledge base that has integrated biomedical knowledge in the form of hundreds of millions of network‐based relations in the RDF format. Metarel was used to upgrade the logical meaning of these relations towards Description Logic. This has enabled to build a computer reasoner that could run over the knowledge base and derive new knowledge statements

A semantic framework for ontology usage analysis

The Semantic Web envisions a Web where information is accessible and processable by computers as well as humans. Ontologies are the cornerstones for realizing this vision of the Semantic Web by capturing domain knowledge by defining the terms and the relationship between these terms to provide a formal representation of the domain with machine-understandable semantics. Ontologies are used for semantic annotation, data interoperability and knowledge assimilation and dissemination.In the literature, different approaches have been proposed to build and evolve ontologies, but in addition to these, one more important concept needs to be considered in the ontology lifecycle, that is, its usage. Measuring the “usage” of ontologies will help us to effectively and efficiently make use of semantically annotated structured data published on the Web (formalized knowledge published on the Web), improve the state of ontology adoption and reusability, provide a usage-based feedback loop to the ontology maintenance process for a pragmatic conceptual model update, and source information accurately and automatically which can then be utilized in the other different areas of the ontology lifecycle. Ontology Usage Analysis is the area which evaluates, measures and analyses the use of ontologies on the Web. However, in spite of its importance, no formal approach is present in the literature which focuses on measuring the use of ontologies on the Web. This is in contrast to the approaches proposed in the literature on the other concepts of the ontology lifecycle, such as ontology development, ontology evaluation and ontology evolution. So, to address this gap, this thesis is an effort in such a direction to assess, analyse and represent the use of ontologies on the Web.In order to address the problem and realize the abovementioned benefits, an Ontology Usage Analysis Framework (OUSAF) is presented. The OUSAF Framework implements a methodological approach which is comprised of identification, investigation, representation and utilization phases. These phases provide a complete solution for usage analysis by allowing users to identify the key ontologies, and investigate, represent and utilize usage analysis results. Various computation components with several methods, techniques, and metrics for each phase are presented and evaluated using the Semantic Web data crawled from the Web. For the dissemination of ontology-usage-related information accessible to machines and humans, The U Ontology is presented to formalize the conceptual model of the ontology usage domain. The evaluation of the framework, solution components, methods, and a formalized conceptual model is presented, indicating the usefulness of the overall proposed solution