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BigText-QA: Question Answering over a Large-Scale Hybrid Knowledge Graph

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Big Data Technologies and Applications (BDTA 2023)

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Abstract

Answering complex questions over textual resources remains a challenge, particularly when dealing with nuanced relationships between multiple entities expressed within natural-language sentences. To this end, curated knowledge bases (KBs) like YAGO, DBpedia, Freebase, and Wikidata have been widely used and gained great acceptance for question-answering (QA) applications in the past decade. While these KBs offer a structured knowledge representation, they lack the contextual diversity found in natural-language sources. To address this limitation, BigText-QA introduces an integrated QA approach, which is able to answer questions based on a more redundant form of a knowledge graph (KG) that organizes both structured and unstructured (i.e., “hybrid”) knowledge in a unified graphical representation. Thereby, BigText-QA is able to combine the best of both worlds—a canonical set of named entities, mapped to a structured background KB (such as YAGO or Wikidata), as well as an open set of textual clauses providing highly diversified relational paraphrases with rich context information. Our experimental results demonstrate that BigText-QA outperforms DrQA, a neural-network-based QA system, and achieves competitive results to QUEST, a graph-based unsupervised QA system.

Supported by Luxembourg National Research Fund (FNR).

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Notes

  1. 1.

    We use Wikipedia articles here to keep aligned with the experiments described in this paper. Note however that the choice of the document collection is not limited to any particular document type and can also combine heterogeneous natural-language resources, such as books, news, social networks, etc.

  2. 2.

    https://spacy.io/.

  3. 3.

    https://dumps.wikimedia.org/enwiki/latest/.

  4. 4.

    https://lucene.apache.org/core/.

  5. 5.

    We use the default word2vec model [36] trained on Google news.

  6. 6.

    This decision was motivated by the fact that, for those questions, none of the top-10 documents returned by Lucene actually contained the answer.

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Acknowledgments

This work was funded by FNR (Grant ID: 15748747). We thank Rishiraj Saha Roy and his group at the Max Planck Institute for Informatics for their helpful discussions and their support on integrating QUEST with our BigText graph.

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Authors and Affiliations

  1. University of Luxembourg, 4365, Esch-sur-Alzette, Luxembourg

    Jingjing Xu, Maria Biryukov & Martin Theobald

  2. International Institute of Information Technology (IIIT), Bangalore, India

    Vinu Ellampallil Venugopal

Authors
  1. Jingjing Xu
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  2. Maria Biryukov
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  3. Martin Theobald
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  4. Vinu Ellampallil Venugopal
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Correspondence to Jingjing Xu .

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Editors and Affiliations

  1. Edinburgh Napier University, Edinburgh, UK

    Zhiyuan Tan

  2. University of Bristol, Bristol, UK

    Yulei Wu

  3. University of Technology Sydney, Sydney, NSW, Australia

    Min Xu

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Xu, J., Biryukov, M., Theobald, M., Venugopal, V.E. (2024). BigText-QA: Question Answering over a Large-Scale Hybrid Knowledge Graph. In: Tan, Z., Wu, Y., Xu, M. (eds) Big Data Technologies and Applications. BDTA 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 555. Springer, Cham. https://doi.org/10.1007/978-3-031-52265-9_3

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  • DOI: https://doi.org/10.1007/978-3-031-52265-9_3

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