Knowledge merging under multiple attributes

Authors:
Bo Wei;Zhi Jin;Didar Zowghi
Affiliations:
MADIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China;MADIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China and Key Laboratory of High Confidence Software Technologies, Ministry of Education, Peking University ...;Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia
Venue:
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Year:
2010

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Abstract

Knowledge merging is the process of synthesizing multiple knowledge models into a common model. Available methods concentrate on resolving conflicting knowledge. While, we argue that besides the inconsistency, some other attributes may also affect the resulting knowledge model. This paper proposes an approach for knowledge merging under multiple attributes, i.e. Consistency and Relevance. This approach introduces the discrepancy between two knowledge models and defines different discrepancy functions for each attribute. An integrated distance function is used for assessing the candidate knowledge models.