This paper brings a contribution focused on collaborative engineering projects where knowledge plays a key role in the process. Collaboration is the arena, engineering projects are the target, knowledge is the currency used to provide harmony into the arena since it can potentially support innovation and, hence, a successful collaboration. The building and construction domain is challenged with significant problems for exchanging, sharing and integrating information between actors. For example, semantic gaps or lack of meaning definition at the conceptual and technical level, are problems fundamentally created through the employment of representations to map the ‘world’ into models in an endeavour to anticipate different actors’ views, vocabulary, and objectives. One of the primary research challenges addressed in this work is the process of formalization and representation of document content, where most existing approaches are limited in their capability 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 by incorporation of implicit information derived from the complex relationships (the Semantic Associations) modelled by domain ontologies combined with the information presented in documents, thereby providing a baseline for facilitating knowledge interpretation and sharing between humans and machines. The paper introduces a novel conceptual framework for representation of knowledge sources, where each knowledge source is semantically represented (within its domain of use) by a Semantic Vector. This work contributes to the enrichment of Semantic Vectors, using the classical vector space model approach extended with ontological support, employing ontology concepts and their relations in the enrichment process. The test bed for the assessment of the approach is the Building and Construction industry, using an appropriateB&CdomainOntology. 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.
- Knowledge sharing
- Ontology engineering
- Semantic interoperability
- Unsupervised document classification
- Vector space models