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Updated: Jul 31, 2025

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The SciQA Scientific Question Answering Benchmark for Scholarly Knowledge.

Sören Auer1,2, Dante A C Barone3, Cassiano Bartz3

  • 1TIB-Leibniz Information Centre for Science and Technology, Hannover, Germany.

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|May 4, 2023
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Summary
This summary is machine-generated.

This study introduces SciQA, a new benchmark for scientific question answering (QA) using scholarly knowledge graphs. SciQA challenges next-generation QA systems with complex questions derived from the Open Research Knowledge Graph.

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Area of Science:

  • Computer Science
  • Information Science
  • Artificial Intelligence

Background:

  • Knowledge graphs are increasingly popular but often lack semantic depth.
  • Existing question answering (QA) benchmarks primarily focus on encyclopedic knowledge graphs like Wikidata.
  • Scholarly knowledge graphs offer a richer, more complex data source for advanced QA.

Purpose of the Study:

  • To introduce SciQA, a novel benchmark for scientific question answering (QA) tailored for scholarly knowledge.
  • To evaluate the capabilities of next-generation QA systems on complex, research-oriented queries.
  • To advance the field of QA by providing a challenging dataset for scholarly information retrieval.

Main Methods:

  • Leveraged the Open Research Knowledge Graph (ORKG) containing ~170,000 resources from ~15,000 scholarly articles.
  • Developed 100 complex questions manually through a bottom-up approach.
  • Generated an additional 2,465 questions using eight templates, translated into SPARQL queries.

Main Results:

  • The SciQA benchmark comprises 2,565 questions covering diverse research fields and query types.
  • Preliminary evaluations indicate SciQA presents a significant challenge for current QA systems.
  • The benchmark is integrated into the Scholarly Question Answering over Linked Data (QALD) Challenge at ISWC 2023.

Conclusions:

  • SciQA advances scientific QA by providing a benchmark specifically designed for the complexities of scholarly knowledge.
  • The benchmark facilitates the development and evaluation of more sophisticated QA systems capable of handling research-oriented data.
  • SciQA contributes to the broader Semantic Web community by enabling advanced data querying and analysis in scientific domains.