AI Magazine
KADS: a modelling approach to knowledge engineering
Knowledge Acquisition - Special issue on the KADS approach to knowledge engineering
Logical foundations of object-oriented and frame-based languages
Journal of the ACM (JACM)
Task modeling with reusable problem-solving methods
Artificial Intelligence
The Knowledge Acquisition and Representation Language, KARL
IEEE Transactions on Knowledge and Data Engineering
UPML: A Framework for Knowledge System Reuse
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
The Knowledge Engineering Review
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Applying problem solving methods for process knowledge acquisition, representation, and reasoning
Proceedings of the 4th international conference on Knowledge capture
The art of artificial intelligence: themes and case studies of knowledge engineering
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
Expert Systems with Applications: An International Journal
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The development of knowledge-based systems is usually approached through the combined skills of software and knowledge engineers (SEs and KEs, respectively) and of subject matter experts (SMEs). One of the most critical steps in this task aims at transferring knowledge from SMEs' expertise to formal, machine-readable representations, which allow systems to reason with such knowledge. However, this process is costly and error prone. Alleviating such knowledge acquisition bottleneck requires enabling SMEs with the means to produce the target knowledge representations, minimizing the intervention of KEs. This is especially difficult in the case of complex knowledge types like processes. The analysis of scientific domains like Biology, Chemistry, and Physics uncovers: (i) that process knowledge is the single most frequent type of knowledge occurring in such domains and (ii) specific solutions need to be devised in order to allow SMEs to represent it in a computational form. We present a framework and computer system for the acquisition and representation of process knowledge in scientific domains by SMEs. We propose methods and techniques to enable SMEs to acquire process knowledge from the domains, to formally represent it, and to reason about it. We have developed an abstract process metamodel and a library of problem solving methods (PSMs), which support these tasks, respectively providing the terminology for SME-tailored process diagrams and an abstract formalization of the strategies needed for reasoning about processes. We have implemented this approach as part of the DarkMatter system and formally evaluated it in the context of the intermediate evaluation of Project Halo, an initiative aiming at the creation of question answering systems by SMEs.