An Ensemble Matchers Based Rank Aggregation Method for Taxonomy Matching

Hailun Lin, Yuanzhuo Wang, Yantao Jia, Jinhua Xiong, Peng Zhang, Xueqi Cheng

Abstract: Taxonomy matching is an important operation of knowledge base merging. Several matchers for automating taxonomy matching have been proposed and evaluated in the knowledge base community. Studies reveal that there is no single taxonomy matcher suitable for any domain-specific taxonomy mapping, therefore an ensemble of taxonomy matchers is essential. In this paper, we propose taxonomy metamatching, a distributed computing framework for assembling taxonomy matchers and generating an optimal taxonomy mapping. And we introduce TRA, a Threshold Rank Aggregation algorithm for this problem. Experimental results show that TRA outperforms state-of-the-art approaches regardless of domains and scales of taxonomies, which demonstrates that TRA performs good adaptability to taxonomy matching.