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We developed guidelines and a tool called Mindtagger to help debug and improve knowledge base construction systems. This system aids in extracting high-quality information from unstructured data through iterative data improvement and systematic labeling.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Data Science

Background:

  • End-to-end knowledge base construction systems automatically extract domain-specific information from unstructured data using statistical inference.
  • Deploying such systems, like the DeepDive framework, reveals challenges in debugging and enhancing their quality.
  • These systems often involve an iterative development cycle requiring user input for data improvement.

Purpose of the Study:

  • To address challenges in debugging and improving end-to-end knowledge base construction systems.
  • To develop principles for analyzing system errors and provide tools for data inspection and labeling.
  • To facilitate more productive and systematic data labeling for knowledge base refinement.

Main Methods:

  • Developed guidelines for error analysis based on best practices, emphasizing data labeling.
  • Created Mindtagger, a versatile tool for systematic and productive data labeling.
  • Demonstrated specific data labeling tasks within the error analysis guidelines using Mindtagger.

Main Results:

  • Established principles and guidelines for analyzing errors in knowledge base construction systems.
  • Introduced Mindtagger, a configurable tool supporting diverse data labeling tasks.
  • Showcased the practical application of Mindtagger in conjunction with error analysis guidelines.

Conclusions:

  • Systematic data labeling is crucial for analyzing and improving end-to-end knowledge base construction systems.
  • Mindtagger provides effective tooling to support data labeling in the iterative refinement of knowledge bases.
  • The developed approach enhances the quality and reliability of automatically constructed knowledge bases.