Methods for knowledge acquisition and refinement in second generation expert systems
ACM SIGART Bulletin - Special issue on knowledge acquisition
An Approach to Knowledge Acquisition Based on the Structure of Personal Construct Systems
IEEE Transactions on Knowledge and Data Engineering
Capturing Knowledge Through Top-Down Induction of Decision Trees
IEEE Expert: Intelligent Systems and Their Applications
MnM: Ontology Driven Semi-automatic and Automatic Support for Semantic Markup
EKAW '02 Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web
Temporal abstraction in intelligent clinical data analysis: A survey
Artificial Intelligence in Medicine
Journal of Biomedical Informatics
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Agent-oriented programming: from prolog to guarded definite clauses
Agent-oriented programming: from prolog to guarded definite clauses
A decision support system for cost-effective diagnosis
Artificial Intelligence in Medicine
Editorial: 25 Years of Knowledge Acquisition
International Journal of Human-Computer Studies
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The knowledge engineer practices the art of bringing the principles and tools of AI research to bear on difficult applications problems requiring experts'' knowledge for their solution. The technical issues of acquiring this knowledge, representing it, and using it appropriately to construct and explain lines-of-reasoning, are important problems in the design of knowledge-based systems. Various systems that have achieved expert level performance in scientific and medical inference illuminates the art of knowledge engineering and its parent science, Artificial Intelligence.