Tolerance Rough Set Based Attribute Extraction Approach for Multiple Semantic Knowledge Base Integration

In the integration of multiple semantic knowledge bases (SKBs), the inconsistence of the items or their attributes appeared in different SKBs is still an opening challenge for researchers. To address this issue, this paper presents an innovative approach which bases on extracting common class attributes and establishing unified category-attribute templates. Since the natural properties of uncertainty and vagueness of semantic analysis involved in selecting a specific attribute from numerous candidates, the tolerance rough set (TRS) techniques are applied in constructing class-attribute templates from online SKBs. The extraction of attribute is fulfilled by statistical techniques and is integrated into the TRS framework. Finally, experiments are conducted on random selected categories. Experimental results show the effectiveness of the proposed approach.

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