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orkg/SciQA

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@Article{SciQA2023,
  author={Auer, S{\"o}ren
  and Barone, Dante A. C.
  and Bartz, Cassiano
  and Cortes, Eduardo G.
  and Jaradeh, Mohamad Yaser
  and Karras, Oliver
  and Koubarakis, Manolis
  and Mouromtsev, Dmitry
  and Pliukhin, Dmitrii
  and Radyush, Daniil
  and Shilin, Ivan
  and Stocker, Markus
  and Tsalapati, Eleni},
  title={The SciQA Scientific Question Answering Benchmark for Scholarly Knowledge},
  journal={Scientific Reports},
  year={2023},
  month={May},
  day={04},
  volume={13},
  number={1},
  pages={7240},
  abstract={Knowledge graphs have gained increasing popularity in the last decade in science and technology. However, knowledge graphs are currently relatively simple to moderate semantic structures that are mainly a collection of factual statements. Question answering (QA) benchmarks and systems were so far mainly geared towards encyclopedic knowledge graphs such as DBpedia and Wikidata. We present SciQA a scientific QA benchmark for scholarly knowledge. The benchmark leverages the Open Research Knowledge Graph (ORKG) which includes almost 170,000 resources describing research contributions of almost 15,000 scholarly articles from 709 research fields. Following a bottom-up methodology, we first manually developed a set of 100 complex questions that can be answered using this knowledge graph. Furthermore, we devised eight question templates with which we automatically generated further 2465 questions, that can also be answered with the ORKG. The que

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dataset_licence({ "id": "orkg/SciQA" })
signal: "permissive"
stated_licence: "cc-by-4.0"
sources: 0