TY - JOUR
T1 - Refining Automatically Extracted Knowledge Bases Using Crowdsourcing
AU - Li, Chunhua
AU - Zhao, Pengpeng
AU - Sheng, Victor S.
AU - Xian, Xuefeng
AU - Wu, Jian
AU - Cui, Zhiming
N1 - Publisher Copyright:
© 2017 Chunhua Li et al.
PY - 2017
Y1 - 2017
N2 - 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 improvement 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.
AB - 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 improvement 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.
UR - http://www.scopus.com/inward/record.url?scp=85020037440&partnerID=8YFLogxK
U2 - 10.1155/2017/4092135
DO - 10.1155/2017/4092135
M3 - Article
C2 - 28588611
AN - SCOPUS:85020037440
SN - 1687-5265
VL - 2017
JO - Computational Intelligence and Neuroscience
JF - Computational Intelligence and Neuroscience
M1 - 4092135
ER -