Paper Info
Title
ABML knowledge refinement loop: a case study
Abstract
Argument Based Machine Learning (ABML) was recently demonstrated to offer significant benefits for knowledge elicitation. In knowledge acquisition, ABML is used by a domain expert in the so-called ABML knowledge refinement loop. This draws the expert's attention to the most critical parts of the current knowledge base, and helps the expert to argue about critical concrete cases in terms of the expert's own understanding of such cases. Knowledge elicited through ABML refinement loop is therefore more consistent with expert's knowledge and thus leads to more comprehensible models in comparison with other ways of knowledge acquisition with machine learning from examples. Whereas the ABML learning method has been described elsewhere, in this paper we concentrate on detailed mechanisms of the ABML knowledge refinement loop. We illustrate these mechanisms with examples from a case study in the acquisition of neurological knowledge, and provide quantitative results that demonstrate how the model evolving through the ABML loop becomes increasingly more consistent with the expert's knowledge during the process.
Year
DOI
Venue
2012
10.1007/978-3-642-34624-8_5
ISMIS
Keywords
Field
DocType
neurological knowledge,critical concrete case,abml refinement loop,current knowledge base,abml knowledge refinement loop,abml loop,so-called abml knowledge refinement,knowledge elicitation,knowledge acquisition,domain expert,case study
Data mining,Computer science,Subject-matter expert,Knowledge management,Knowledge engineer,Artificial intelligence,Knowledge base,Knowledge elicitation,Machine learning,Knowledge acquisition
Conference
Citations 
PageRank 
References 
1
0.35
7
Authors
7
Name
Order
Citations
PageRank
Matej Guid16412.91
Martin Možina222316.90
Vida Groznik3193.45
Dejan Georgiev4192.97
Aleksander Sadikov5539.96
Zvezdan Pirtošek691.64
Ivan Bratko71526405.03