References in periodicals archive ?
The knowledge about Ford's processes is contained in an ontology originally developed using the KL-ONE representation language and methodology.
In the case of AIRS and RLF, the representational framework is based on KL-ONE .
Unfortunately, because of the power of KL-ONE , a complete classification algorithm is impossible.
An overview of the KL-ONE knowledge representation system.
The knowledge about Ford's manufacturing processes is contained in an ontology originally developed using the KL-ONE representation language and methodology.
This was done by creating an implementation of the KL-ONE language using the LISP programming language and developing a classifier that could reason with the ontology.
As the semantic web architecture and standards were developed, it became obvious that the Global Study Process Allocation System (GSPAS) KL-ONE ontology would be much more usable and of better value to Ford if it could be rewritten into OWL/RDF.
The IITM team delivered a reengineered OWL/RDF ontology that contained the knowledge in the existing KL-ONE ontology.
In this article, we refer to the GSPAS KL-ONE ontology as GSPAS KB or as GSPAS ontology or as KL-ONE ontology, and refer to the reengineered GSPAS OWL ontology as new ontology or as OWL ontology.
Each build step in the process sheet is parsed and transformed into a KL-ONE description, which is then classified to find the matching concepts in GSPAS KB.
Throughout the intervening years and all of the changes, the KL-ONE ontology model and classifier proved to be robust enough to support GSPAS and Ford's manufacturing plants.
Ford adapted the KL-ONE knowledge representation system during its initial development of DLMS.
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