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ECAI
2008
Springer

Fighting Knowledge Acquisition Bottleneck with Argument Based Machine Learning

8 years 4 months ago
Fighting Knowledge Acquisition Bottleneck with Argument Based Machine Learning
Knowledge elicitation is known to be a difficult task and thus a major bottleneck in building a knowledge base. Machine learning has long ago been proposed as a way to alleviate this problem. Machine learning usually helps the domain expert to uncover some of the more tacit concepts. However, the learned concepts are often hard to understand and hard to extend. A common view is that a combination of a domain expert and machine learning would yield the best results. Recently, argument based machine learning (ABML) has been introduced as a combination of argumentation and machine learning. Through argumentation, ABML enables the expert to articulate his knowledge easily and in a very natural way. ABML was shown to significantly improve the comprehensibility and accuracy of the learned concepts. This makes ABML a most natural tool for constructing a knowledge base. The present paper shows how this is accomplished through a case study of building a knowledge base of an expert system used i...
Martin Mozina, Matej Guid, Jana Krivec, Aleksander
Added 19 Oct 2010
Updated 19 Oct 2010
Type Conference
Year 2008
Where ECAI
Authors Martin Mozina, Matej Guid, Jana Krivec, Aleksander Sadikov, Ivan Bratko
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