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.
He has worked for many yers on knowledge representation theory and applications, highlighted by early work on KL-One and recent work on CLASSIC.
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.
This was the KL-ONE project that Ron Brachman and I and many other colleagues worked on (Brachman and Schmolze 1985).
The original goals that I had for the KL-ONE project were quite ambitious.
Eventually the complexity of KL-ONE's many goals led Ron Brachman and Hector Levesque to focus on some specific problems relating to expressivity, and they discovered the first expressivity/complexity trade-off (Brachman and Levesque 1987).
Prior to joining Bell Labs in 1985, Brachman was instrumental in the design and implementation of several well-known knowledge representation systems, including KL-One, Krypton, and CLASSIC, and his work formed the basis for an entire subfield of research in AI (description logics).
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