Mining mathematical documents for question answering via unsupervised formula labeling

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AIZAWA, Akiko, ed. and others. JCDL '22 : Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2022. New York: ACM, 2022, 19. ISBN 978-1-4503-9345-4. Available under: doi: 10.1145/3529372.3530925
Zusammenfassung

The increasing number of questions on Question Answering (QA) platforms like Math Stack Exchange (MSE) signifies a growing information need to answer math-related questions. However, there is currently very little research on approaches for an open data QA system that retrieves mathematical formulae using their concept names or querying formula identifier relationships from knowledge graphs. In this paper, we aim to bridge the gap by presenting data mining methods and benchmark results to employ Mathematical Entity Linking (MathEL) and Unsupervised Formula Labeling (UFL) for semantic formula search and mathematical question answering (MathQA) on the arXiv preprint repository, Wikipedia, and Wikidata. The new methods extend our previously introduced system, which is part of the Wikimedia ecosystem of free knowledge. Based on different types of information needs, we evaluate our system in 15 information need modes, assessing over 7,000 query results. Furthermore, we compare its performance to a commercial knowledge-base and calculation-engine (Wolfram Alpha) and search-engine (Google). The open source system is hosted by Wiki-media at https://mathqa.wmflabs.org. A demovideo is available at purl.org/mathqa.

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004 Informatik
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Joint Conference on Digital Libraries, JCDL '22, 20. Juni 2022 - 24. Juni 2022, Cologne, Germany and Online (Hybrid)
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Zitieren
ISO 690SCHARPF, Philipp, Moritz SCHUBOTZ, Bela GIPP, 2022. Mining mathematical documents for question answering via unsupervised formula labeling. Joint Conference on Digital Libraries, JCDL '22. Cologne, Germany and Online (Hybrid), 20. Juni 2022 - 24. Juni 2022. In: AIZAWA, Akiko, ed. and others. JCDL '22 : Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2022. New York: ACM, 2022, 19. ISBN 978-1-4503-9345-4. Available under: doi: 10.1145/3529372.3530925
BibTex
@inproceedings{Scharpf2022Minin-59116,
  year={2022},
  doi={10.1145/3529372.3530925},
  title={Mining mathematical documents for question answering via unsupervised formula labeling},
  isbn={978-1-4503-9345-4},
  publisher={ACM},
  address={New York},
  booktitle={JCDL '22 : Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2022},
  editor={Aizawa, Akiko},
  author={Scharpf, Philipp and Schubotz, Moritz and Gipp, Bela},
  note={Article Number: 19}
}
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