Knowledge Merging under Multiple Attributes

Publisher:
Springer-Verlag Berlin Heidelberg
Publication Type:
Conference Proceeding
Citation:
Lecture Notes in Artificial Intelligence 6291 - Knowledge Science, Engineering and Management, 2010, pp. 555 - 560
Issue Date:
2010-01
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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.
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