Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Information disclosure in evolving information systems: taking a shot at a moving target
Data & Knowledge Engineering
“Reducing” CLASSIC to practice: knowledge representation theory meets reality
Artificial Intelligence - Special issue on applications of artificial intelligence
Contradictions and critical issues during system evolution
Proceedings of the 2002 ACM symposium on Applied computing
Reorganizing Knowledge to Improve Performance
IEEE Transactions on Knowledge and Data Engineering
An ontological basis for computer aided innovation
Computers in Industry
Matching of different abstraction level knowledge sources: the case of inventive design
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part IV
An ontology-based approach for inventive problem solving
Engineering Applications of Artificial Intelligence
International Journal of Knowledge-based and Intelligent Engineering Systems
Facilitating the resolution of inventive problems using semantic relatedness and ontology reasoning
International Journal of Knowledge-based and Intelligent Engineering Systems - Selected papers of KES2012-Part 1 of 2
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Driving problem solving process in R&D departments rely on along historical set of experiences gained through practices,methods acquisition and continuous improvement. This improvementalways needs to be enhanced by integrating new paradigms inaccordance with global industrial situation. This situation iscurrently turned towards innovation concerns and among otherimposes R&D departments to improve the robustness of theirdecisions. We propose to demonstrate how R&D choices can bedriven by representing problems through a parameter network andextract from this parameter network a set of key contradictions tobe solved to drive R&D decisions inventively.