Refining Automatically Extracted Knowledge Bases Using Crowdsourcing

Chunhua Li, Pengpeng Zhao, Victor S. Sheng, Xuefeng Xian, Jian Wu, Zhiming Cui
2017 Computational Intelligence and Neuroscience  
Machine-constructed knowledge bases often contain noisy and inaccurate facts. There exists significant work in developing automated algorithms for knowledge base refinement. Automated approaches improve the quality of knowledge bases but are far from perfect. In this paper, we leverage crowdsourcing to improve the quality of automatically extracted knowledge bases. As human labelling is costly, an important research challenge is how we can use limited human resources to maximize the quality
more » ... ovement for a knowledge base. To address this problem, we first introduce a concept of semantic constraints that can be used to detect potential errors and do inference among candidate facts. Then, based on semantic constraints, we propose rank-based and graph-based algorithms for crowdsourced knowledge refining, which judiciously select the most beneficial candidate facts to conduct crowdsourcing and prune unnecessary questions. Our experiments show that our method improves the quality of knowledge bases significantly and outperforms state-of-the-art automatic methods under a reasonable crowdsourcing cost.
doi:10.1155/2017/4092135 pmid:28588611 pmcid:PMC5446892 fatcat:ntycm4lnrrfobmrvmone6fks64