A framework and computer system for knowledge-level acquisition, representation, and reasoning with process knowledge

• Published in: International journal of human-computer studies Vol. 68; no. 10; pp. 641 - 668
• Main Authors: Gómez-Pérez, José Manuel, Erdmann, Michael, Greaves, Mark, Corcho, Oscar, Benjamins, Richard
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.10.2010; Elsevier
• Subjects: Acquisitions; Applied sciences; Artificial intelligence; Computer programs; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Evaluation; Exact sciences and technology; Knowledge acquisition; Knowledge base; Knowledge representation; Marketing; Metamodels; Problem solving methods; Process knowledge; Reasoning; Representations; Software; Software engineering; Speech and sound recognition and synthesis. Linguistics; Subject matter experts; Tasks; Usability; Process knowledge; Evaluation; Subject matter experts; Knowledge acquisition; Reasoning; Knowledge representation; Problem solving methods; Usability; Project evaluation; Bottleneck; Terminology; Software development; Knowledge base; Biology; Legibility; Acquisition process; Linguistics; Problem solving; System representation; Metamodel; Responder
• ISSN: 1071-5819; 1095-9300
• DOI: 10.1016/j.ijhcs.2010.05.004

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.