Clustering Analysis-Based Approach to Detecting Entity Mixture in Knowledge Bases

Haihua Xie, Xiaoqing Lu, Zhi Tang

Abstract: Entity Mixture refers to a phenomenon that the information on an entity is mistaken as attributes of another entity in information extraction during knowledge base (KB) construction and population. To improve the quality of knowledge-based services, data accuracy and validity in KBs should be enhanced. This paper presents a clustering analysis-based approach for detecting potentially mixed entities in a KB. Our approach aims at detecting the inconsistency of the attribute values of a KB instance as an indication of entity mixture occurrence. This paper also presents an experiment conducted on a data set of industrial applications to demonstrate the process of entity mixture detection. Experimental results show that our proposed methodology performs well in detecting mixed entities.