Management of Knowledge Sources Supported by Domain Ontologies: Building and Construction Case Studys

• Autores: Ruben Costa, Celson Lima
• Localización: Intelligent systems in accounting, finance & management, ISSN-e 1099-1174, Vol. 22, Nº. 1, 2015, págs. 29-64
• Idioma: inglés
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• Resumen

  • This paper introduces a novel conceptual framework to support the creation of knowledge representations based on enriched semantic vectors, using the classical vector space model approach extended with ontological support. This work is focused on collaborative engineering projects where knowledge plays a key role in the process. Collaboration is the arena, engineering projects are the target and knowledge is the currency used to provide harmony into the arena since it can potentially support innovation and, hence, a successful collaboration. The test bed for the assessment of the approach comes from the Building and Construction sector, which is challenged with significant problems for exchanging, sharing and integrating information among actors. Semantic gaps or lack of meaning definition at the conceptual and technical levels, for example, are problems fundamentally originated through the employment of representations to map the ‘world’ into models in an endeavour to anticipate other actors’ views, vocabulary and even motivations. One of the primary research challenges addressed in this work relates to the process of formalization and representation of document contents, where most existing approaches are limited and only take into account the explicit, word-based information in the document. The research described in this paper explores how traditional knowledge representations can be enriched through incorporation of implicit information derived from the complex relationships (semantic associations) modelled by domain ontologies with the addition of information presented in documents, by providing a baseline for facilitating knowledge interpretation and sharing between humans and machines. Preliminary results were collected using a clustering algorithm for document classification, which indicates that the proposed approach does improve the precision and recall of classifications. Future work and open issues are also discussed