Abstract
This paper discusses ontologies, methods for developing them and languages for representing them. A special ontology for computational support of the Conceptual ReDesign Process (CRDP) is introduced with a simple illustrative example of an application. The ontology denoted as Global context (GLB) combines features of general semantic networks and features of UML language. The ontology is task-oriented and domain-oriented, and contains three basic strata – GLBExpl(stratum of Explanation), GLBFAct (stratum of Fields of Activities) and GLBEnv (stratum of Environment), with their sub-strata. The ontology has been developed to represent functions of systems and their components in CRDP. The main difference between this ontology and ontologies which have been developed to identify functions (the semantic details in those ontologies must be as deep as possible) is in the style of the description of the functions. In the proposed ontology, Formation Spaces were used as lower semantic categories the semantic deepness of which is variable and depends on the actual solution approach of a specialised Conceptual Designer.
Highlights
The approach of Artificial Intelligence disciplines to support for design problem solution synthesis has changed qualitatively in recent times
Essential points for specifying ontology include: l Purpose and objective of the development and application of the ontology. l Subject domains and tasks relevant for the development and application of the ontology. l Classes, relations, functions and other formal categories which will be used for conceptualisation. l Way of working with the ontology, the form of computer support and the user of the ontology. l Terminology used in the ontology (“schools”, traditions and usage). l Implementation environment in which the ontology will be developed and applied
CYCL language is a language of the CYC project. It is based on LISP syntax and, like KIF, it follows the features of predicate first order language. (Some parts of the developed ontology–6000 concepts and 60 000 assertions–are available in [12].) Of the many of other languages for representation of ontologies, the following are widely used: OCML (Ontology Compositional Modelling Language), DAML-ONT (Darpa Agent Mark-Up Language-ONTology), OIL (Ontology Inference Layer) and DAML+OIL
Summary
The approach of Artificial Intelligence disciplines to support for design problem solution synthesis has changed qualitatively in recent times. The traditional interest in sophisticated formal means for system design (description of components, procedures of system synthesis from components, etc.) is targeted at semantic modelling and at an effective description of the functions of the designed systems. The field which promises the necessary improvements in modelling the semantics is the field of ontologies
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