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Big Data Technologies and Applications. 13th EAI International Conference, BDTA 2023, Edinburgh, UK, August 23-24, 2023, Proceedings

Research Article

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

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BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-52265-9_3,
        author={Jingjing Xu and Maria Biryukov and Martin Theobald and Vinu Ellampallil Venugopal},
        title={BigText-QA: Question Answering over a Large-Scale Hybrid Knowledge Graph},
        proceedings={Big Data Technologies and Applications. 13th EAI International Conference, BDTA 2023, Edinburgh, UK, August 23-24, 2023, Proceedings},
        proceedings_a={BDTA},
        year={2024},
        month={1},
        keywords={Question Answering Large-Scale Graph Hybrid Knowledge Graph Natural Language Processing},
        doi={10.1007/978-3-031-52265-9_3}
    }
    
  • Jingjing Xu
    Maria Biryukov
    Martin Theobald
    Vinu Ellampallil Venugopal
    Year: 2024
    BigText-QA: Question Answering over a Large-Scale Hybrid Knowledge Graph
    BDTA
    Springer
    DOI: 10.1007/978-3-031-52265-9_3
Jingjing Xu1,*, Maria Biryukov1, Martin Theobald1, Vinu Ellampallil Venugopal
  • 1: University of Luxembourg
*Contact email: jingjing.xu@uni.lu

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—acanonical set of named entities, mapped to a structured background KB (such as YAGO or Wikidata), as well as anopen set of textual clausesproviding 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.

Keywords
Question Answering Large-Scale Graph Hybrid Knowledge Graph Natural Language Processing
Published
2024-01-31
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-52265-9_3
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