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Creating and validating a scholarly knowledge graph using natural language processing and microtask crowdsourcing.

Allard Oelen1, Markus Stocker1, Sören Auer1

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Summary
This summary is machine-generated.

TinyGenius validates natural language processing (NLP) extracted scholarly knowledge using crowdsourced microtasks. This approach enhances the accuracy of scholarly knowledge graphs, despite varying participant agreement across different validation tasks.

Keywords:
Crowdsourcing microtasksKnowledge graph validationScholarly knowledge graphsUser interface evaluation

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Area of Science:

  • Computer Science
  • Information Science
  • Scholarly Communication

Background:

  • The increasing volume of scholarly publications necessitates efficient methods for knowledge discovery and organization.
  • Scholarly knowledge graphs offer a structured, machine-readable format for organizing research findings.
  • Current natural language processing (NLP) methods for knowledge extraction lack the required granularity and accuracy for high-quality data.

Purpose of the Study:

  • To introduce TinyGenius, a methodology for validating NLP-extracted scholarly knowledge statements using crowdsourced microtasks.
  • To populate a paper-centric knowledge graph with validated knowledge using five distinct NLP methods.
  • To enhance the accuracy and granularity of data within scholarly knowledge graphs.

Main Methods:

  • Development and application of the TinyGenius methodology for validating NLP-extracted knowledge.
  • Utilizing crowdsourcing for microtask-based validation of scholarly knowledge statements.
  • Populating a paper-centric knowledge graph using five different NLP extraction techniques.

Main Results:

  • The TinyGenius methodology successfully employed microtasks for validating NLP-extracted scholarly knowledge statements.
  • User evaluation demonstrated the potential of microtasks for statement validation, with varying participant agreement observed across different microtasks.
  • The study provides a detailed discussion of NLP tasks and the data model used within the TinyGenius framework.

Conclusions:

  • Crowdsourced microtasks represent a promising approach for validating NLP-generated scholarly knowledge.
  • The TinyGenius methodology offers a scalable solution to improve the quality of scholarly knowledge graphs.
  • Further research may focus on optimizing microtask design to enhance inter-participant agreement and overall validation accuracy.