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Refining Automatically Extracted Knowledge Bases Using Crowdsourcing.

Chunhua Li1, Pengpeng Zhao1, Victor S Sheng2

  • 1School of Computer Science and Technology, Soochow University, Suzhou 215006, China.

Computational Intelligence and Neuroscience
|June 8, 2017
PubMed
Summary
This summary is machine-generated.

This study uses crowdsourcing to refine machine-built knowledge bases, improving accuracy efficiently. Novel algorithms select the best facts for human review, enhancing data quality with limited resources.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Data Science

Background:

  • Machine-constructed knowledge bases often suffer from inaccuracies and noise.
  • Automated knowledge base refinement methods exist but are imperfect.
  • Crowdsourcing offers a way to improve knowledge base quality but is costly.

Purpose of the Study:

  • To leverage crowdsourcing for enhancing the quality of automatically extracted knowledge bases.
  • To develop methods for maximizing quality improvement using limited human resources.
  • To address the challenge of cost-effective knowledge base refinement.

Main Methods:

  • Introduction of semantic constraints for error detection and inference.
  • Development of rank-based and graph-based algorithms for crowdsourced knowledge refining.
  • Judicious selection of candidate facts for crowdsourcing and pruning of unnecessary questions.

Main Results:

  • Significant improvement in the quality of knowledge bases.
  • Outperformance of state-of-the-art automatic refinement methods.
  • Effective knowledge base refinement under reasonable crowdsourcing costs.

Conclusions:

  • Crowdsourcing, guided by semantic constraints and efficient algorithms, is a powerful approach for knowledge base refinement.
  • The proposed methods offer a cost-effective solution for improving the accuracy of large-scale knowledge bases.
  • This work demonstrates a practical strategy for integrating human intelligence into automated data refinement pipelines.