This thesis describes an investigation into the practical use of ontologies for the development of information systems. Ontologies are formal descriptions of shared knowledge in a domain. An ontology can be used as a specification of an information system because it specifies the knowledge that is required for the tasks the information system has to perform. Sharing and reuse of ontologies across different domains and applications can therefore improve information systems design. Ontologies have been a subject for a lot of research carried out in the artificial intelligence community. Although many ontologies have been developed, they fail to demonstrate that ontologies for large and complex domains can be developed that can be used and reused across different applications. There are three reasons for this: (i) many ontologies have not been used to develop a real-life application, (ii) many ontologies have not been reused for different applications in different domains and (iii)many ontologies are merely taxonomies of domain concepts and fail to capture meta-level and tacit background knowledge.

/pdf/construction-of-engineering-ontologies-for-knowledge-sharing-23efgt3you.pdf

Construction of Engineering Ontologies for Knowledge Sharing and Reuse

The word integration has been used with different
meanings in the ontology field. This article
aims at clarifying the meaning of the word “integration”
and presenting some of the relevant work
done in integration. We identify three meanings of
ontology “integration”: when building a new ontology
reusing (by assembling, extending, specializing
or adapting) other ontologies already available;
when building an ontology by merging several
ontologies into a single one that unifies all of
them; when building an application using one or
more ontologies. We discuss the different meanings
of “integration”, identify the main characteristics
of the three different processes and proposethree words to distinguish among those meanings:integration, merge and use.

/pdf/some-issues-on-ontology-integration-4sixgf1x5g.pdf

Some Issues on Ontology Integration

Technologies for the efficient and effective reuse of ontological knowledge are one of the key success factors for the Semantic Web. Putting aside matters of cost or quality, being reusable is an intrinsic property of ontologies, originally conceived of as a means to enable and enhance the interoperability between computing applications. This article gives an account, based on empirical evidence and real-world findings, of the methodologies, methods and tools currently used to perform ontology-reuse processes. We study the most prominent case studies on ontology reuse, published in the knowledge-/ontology-engineering literature from the early nineties. This overview is complemented by two self-conducted case studies in the areas of eHealth and eRecruitment in which we developed Semantic Web ontologies for different scopes and purposes by resorting to existing ontological knowledge on the Web. Based on the analysis of the case studies, we are able to identify a series of research and development challenges which should be addressed to ensure reuse becomes a feasible alternative to other ontology-engineering strategies such as development from scratch. In particular, we emphasize the need for a context- and task-sensitive treatment of ontologies, both from an engineering and a usage perspective, and identify the typical phases of reuse processes which could profit considerably from such an approach. Further on, we argue for the need for ontology-reuse methodologies which optimally exploit human and computational intelligence to effectively operationalize reuse processes.

Reusing ontologies on the Semantic Web: A feasibility study

Knowledge Management (KM) is crucial to organizational survival, yet is a difficult task requiring large expenditure of resources. Information Technology solutions, such as email, document management and intranets, are proving very useful in certain areas. However, many important problems still exist, providing opportunities for new techniques and tools more oriented towards knowledge. We refer to this as Knowledge Technology. A framework has been developed which has allowed opportunities for Knowledge Technology to be identified in support of five key KM activities: personalization, creation/innovation, codification, discovery and capture/monitor. In developing Knowledge Technology for these areas, methods from knowledge engineering are being explored. Our main work in this area has involved the application and evaluation of existing knowledge for a large intranet system. This, and other case studies, have provided important lessons and insights which have led to ongoing research in ontologies, generic models and process modelling methods. We believe that the evidence presented here shows that knowledge engineering has much to offer KM and can be the basis on which to move towards a Knowledge Technology.

Towards a knowledge technology for knowledge management

An ontology-based methodology for hazard identification and causation analysis

This paper presents an ontology to represent the electrical network from the point of view of diagnosis, ie, an ontology of the elements of the network that are necessary for fault diagnosis The notion of 'ontology', rooted in philosophy, is used in knowledge engineering to describe explicit specifications of conceptualizations, where a conceptualization is a set of definitions of elements in a domain It is a useful notion in knowledge based systems development because it provides the means for describing explicitly the conceptualization behind the knowledge represented in a knowledge base An ontology, in this case for fault diagnosis, is a combination of small-scale ontologies After a detailed analysis of the network and its behaviour, five viewpoints were identified as relevant for diagnosis We generated an ontology for each to characterize more accurately and clearly the elements involved in the problem Each of them focuses on different relevant aspects of the network and has a compact body of knowledge and a clear meaning in the problem They are identified as transport, control, events, alarms, and trips Each is discussed by the authors in relation to fault diagnosis

An ontology for fault diagnosis in electrical networks

Ontologies as vehicles for reuse: a mini-experiment

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An ontology for fault diagnosis in electrical networks

Ontologies as vehicles for reuse: a mini-experiment