Learning domain ontologies from engineering documents for manufacturing knowledge reuse by a biologically inspired approach

Abstract

The evolution of any given product family in manufacturing enterprises follows the epicycles in product lifecycle development, thus generating many useful but unmanageable engineering documents. These documents record nearly all the information about product lifecycle and thus provide domain-specific concepts and significant relations for ontology extraction. A qualified ontology, in turn, facilitates the management and reuse of manufacturing knowledge. Consequently, this paper proposes a biologically inspired adaptive growth (BIAG) approach to learn domain ontologies (DO) from these documents. BIAG is modeled on the growth and development mechanisms of a tree, where genes and three key stages of tree lifecycle including seed formation, root development, and tree growth are reference for DO learning. In BIAG, axioms and constrains that serve the same function as genes of a tree are first generated by a semi-supervised learning algorithm, to control the construction process of DO; then three adaptive growth strategies inspired by the above three stages of tree lifecycle are proposed to extract DO from engineering documents. The results of the DO construction and application examples demonstrate the feasibility and effectiveness of BIAG, which could, therefore, be a good choice for DO construction towards manufacturing knowledge reuse.