Essentials in ontology engineering methodologies languages and tools

In the beginning of the 90s, ontology development was similar to an art: ontology developers did not have clear guidelines on how to build ontologies but only some design criteria to be followed. Work on principles, methods and methodologies, together with supporting technologies and languages, made ontology development become an engineering discipline, the so-called Ontology Engineering.

Ontology Engineering refers to the set of activities that concern the ontology development process and the ontology life cycle, the methods and methodologies for building ontologies, and the tool suites and languages that support them.

Thanks to the work done in the Ontology Engineering field, the development of ontologies within and between teams has increased and improved, as well as the possibility of reusing ontologies in other developments and in final applications. Currently, ontologies are widely used in a Knowledge Engineering, Artificial Intelligence and Computer Science, b applications related to knowledge management, natural language processing, e-commerce, intelligent information integration, information retrieval, database design and integration, bio-informatics, education, and c the Semantic Web, the Semantic Grid, and the Linked Data initiative.

In this paper, we provide an overview of Ontology Engineering, mentioning the most outstanding and used methodologies, languages, and tools for building ontologies.

In addition, we include some words on how all these elements can be used in the Linked Data initiative. Keil, — Cambridge: MIT Press. McGuinness, D. Fikes, J. Rice, and S. The chimaera ontology environment. OWL web ontology language: Overview.

Milton, N. Knowledge acquisition in practice. A step-by-step guide. Decision engineering. London: Springer. Missikof, M. Navigli, and P. Integrated approach to web ontology learning and engineering. Computer 35 11 — Musen, M.

An overview of knowledge acquisition. In Second generation expert systems. David, J. Krivine, and R. Simmons, — Berlin: Springer-Verlag New York. Navigli, R. Learning domain ontologies from document warehouses and dedicated web sites. Computational Linguistics 30 2 — Velardi, A. Cucchiarelli, and F. Automatic ontology learning: Supporting a per-concept evaluation by domain experts.

Nielsen, J. Usability engineering. San Francisco: Morgan Kaufmann. Noy, N. Ontology development A guide to creating your first ontology. Perakath, B. Menzel, R. Mayer, F. Fillion, M. Futrell, P. DeWitte, and M. Ontology capture method IDEF5. Interim technical rept. Pinto, H. Tempich, and S. Saitta, — Reinert, M. Semiotica — Schreiber, G. Akkermans, A. Anjewierden, R. Shadbolt, W. Knowledge engineering and management. The commonKADS methodology.

Cambridge: A Bradford Book. MIT Press. Sclano, F. Termextractor: A web application to learn the common terminology of interest groups and research communities. Segaran, T. Evans, and J. Programming the Semantic Web. Shin, I. Kawamura, H. Nakagawa, K. Nakayama, Y. Tahara, and A. Ontomo: Development of ontology building service.

In Principles of practice in multi-agent systems , ed. Yang, M. Yokoo, T. Ito, Z. Jin, and P. Smith, M. Welty, and D. OWL ontology web language guide. Spyns, P. Meersman, and M. Data modelling versus ontology engineering.

Tang, and R. Applied Ontology 3 1—2 — Standard, I. Ergonomic requirements for office work with visual display terminals vdts — part Guidance on usability. Human-centred design processes for interactive systems. Information technology — measurement and rating of performance of computer-based software systems. Information technology — software product evaluation — part 1: General overview.

Ergonomics of human-system interaction — usability methods supporting human-centred design. Information technology — process assessment — part 1: Concepts and vocabulary. Software and system engineering — guidelines for the design and preparation of user documentation for application software. Software engineering — software product quality requirements and evaluation square — guide to square. Software engineering — software life cycle processes — maintenance.

Systems and software engineering — content of systems and software life cycle process information products documentation. Systems and software engineering — software life cycle processes.

Systems and software engineering — system life cycle processes. Steels, L. Components of expertise. AI Magazine 11 2 — Steve, G. Gangemi, and D. Kangassalo and J. Sugiura, N. Izumi, and T. A support environment for domain ontology development with general ontologies and text corpus. Sure, Y. Methodology, tools and case studies for ontology based knowledge management. Staab, and R. Methodology for development and employment of ontology based knowledge management applications.

In Handbook on ontologies , ed. Staab and R. Berlin: Springer. On-To-Knowledge methodology — final version. Project Deliverable D. A methodology for ontology-based knowledge management. In Towards the Semantic Web. Ontology-driven knowledge management , ed. Davies, D. Fensel, and F. Chichester: Wiley. Temmerman, R. Termontography: Ontology building and the sociocognitive approach to terminology description.

Prague: Matfyz Press. Trog, D. Towards ontological commitments with omega-ridl markup language. In Advances in rule interchange and applications , ed. Desarrollado y gestionado con EPrints. Cambiar idioma. Buscar Buscar documentos en este repositorio. Texto completo Vista Previa. Resumen In the beginning of the 90s, ontology development was similar to an art: ontology developers did not have clear guidelines on how to build ontologies but only some design criteria to be followed.

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