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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.

References

  1. Abujabal, A., Yahya, M., Riedewald, M., Weikum, G.: Automated template generation for question answering over knowledge graphs. In: WWW (2017)

    Google Scholar 

  2. Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: ISWC (2007)

    Google Scholar 

  3. Baudiš, P., Šedivỳ, J.: Modeling of the question answering task in the yodaqa system. In: CLEF (2015)

    Google Scholar 

  4. Berant, J., Chou, A., Frostig, R., Liang, P.: Semantic parsing on freebase from question-answer pairs. In: EMNLP (2013)

    Google Scholar 

  5. Bird, S., Klein, E., Loper, E.: Natural language processing with Python: analyzing text with the natural language toolkit. O’Reilly Media, Inc. (2009)

    Google Scholar 

  6. Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: SIGMOD (2008)

    Google Scholar 

  7. Bordes, A., Chopra, S., Weston, J.: Question answering with subgraph embeddings. arXiv preprint arXiv:1406.3676 (2014)

  8. Chen, D., Fisch, A., Weston, J., Bordes, A.: Reading wikipedia to answer open-domain questions. arXiv preprint arXiv:1704.00051 (2017)

  9. Clark, C., Gardner, M.: Simple and effective multi-paragraph reading comprehension. arXiv preprint arXiv:1710.10723 (2017)

  10. Corro, L.D., Gemulla, R.: Clausie: clause-based open information extraction. In: WWW (2013)

    Google Scholar 

  11. Das, R., Zaheer, M., Reddy, S., McCallum, A.: Question answering on knowledge bases and text using universal schema and memory networks. arXiv preprint arXiv:1704.08384 (2017)

  12. Diefenbach, D., Lopez, V., Singh, K., Maret, P.: Core techniques of question answering systems over knowledge bases: a survey. KAIS (2018)

    Google Scholar 

  13. Dong, L., Lapata, M.: Language to logical form with neural attention. arXiv preprint arXiv:1601.01280 (2016)

  14. Dong, L., Wei, F., Zhou, M., Xu, K.: Question answering over freebase with multi-column convolutional neural networks. In: ACL-IJCNLP (2015)

    Google Scholar 

  15. Fader, A., Zettlemoyer, L., Etzioni, O.: Paraphrase-driven learning for open question answering. In: ACL (2013)

    Google Scholar 

  16. Ferrucci, D., et al.: Building watson: an overview of the deepqa project. AI Mag. (2010)

    Google Scholar 

  17. Fu, B., Qiu, Y., Tang, C., Li, Y., Yu, H., Sun, J.: A survey on complex question answering over knowledge base: recent advances and challenges. arXiv preprint arXiv:2007.13069 (2020)

  18. Garg, N., Konjevod, G., Ravi, R.: A polylogarithmic approximation algorithm for the group steiner tree problem. J. Algorithms (2000)

    Google Scholar 

  19. Hagberg, A., Swart, P., S Chult, D.: Exploring network structure, dynamics, and function using networkx. Technical report, Los Alamos National Lab. (LANL) (2008)

    Google Scholar 

  20. Hao, Y., et al.: An end-to-end model for question answering over knowledge base with cross-attention combining global knowledge. In: ACL (2017)

    Google Scholar 

  21. Hearst, M.A.: Automatic acquisition of hyponyms from large text corpora. In: COLING (1992)

    Google Scholar 

  22. Hoffart, J., et al.: Robust disambiguation of named entities in text. In: EMNLP (2011)

    Google Scholar 

  23. van Hulst, J.M., Hasibi, F., Dercksen, K., Balog, K., de Vries, A.P.: REL: an entity linker standing on the shoulders of giants. In: SIGIR (2020)

    Google Scholar 

  24. Joshi, M., Chen, D., Liu, Y., Weld, D.S., Zettlemoyer, L., Levy, O.: Spanbert: improving pre-training by representing and predicting spans. Trans. Assoc. Comput. Linguistics (2020)

    Google Scholar 

  25. Joshi, M., Choi, E., Weld, D.S., Zettlemoyer, L.: Triviaqa: a large scale distantly supervised challenge dataset for reading comprehension. arXiv preprint arXiv:1705.03551 (2017)

  26. Ju, M., Yu, W., Zhao, T., Zhang, C., Ye, Y.: Grape: knowledge graph enhanced passage reader for open-domain question answering. arXiv preprint arXiv:2210.02933 (2022)

  27. Katz, B., Felshin, S., Lin, J.J., Marton, G.: Viewing the web as a virtual database for question answering. In: New Directions in Question Answering (2004)

    Google Scholar 

  28. Kolluru, K., Adlakha, V., Aggarwal, S., Mausam, Chakrabarti, S.: OpenIE6: iterative grid labeling and coordination analysis for open information extraction. In: EMNLP (2020)

    Google Scholar 

  29. Lan, Y., Jiang, J.: Query graph generation for answering multi-hop complex questions from knowledge bases. In: ACL (2020)

    Google Scholar 

  30. Lee, K., He, L., Zettlemoyer, L.: Higher-order coreference resolution with coarse-to-fine inference. CoRR (2018)

    Google Scholar 

  31. Li, B.Z., Min, S., Iyer, S., Mehdad, Y., Yih, W.: Efficient one-pass end-to-end entity linking for questions. In: EMNLP (2020)

    Google Scholar 

  32. Lu, X., Pramanik, S., Saha Roy, R., Abujabal, A., Wang, Y., Weikum, G.: Answering complex questions by joining multi-document evidence with quasi knowledge graphs. In: SIGIR (2019)

    Google Scholar 

  33. Luo, K., Lin, F., Luo, X., Zhu, K.: Knowledge base question answering via encoding of complex query graphs. In: EMNLP (2018)

    Google Scholar 

  34. Manning, C.D.: An introduction to information retrieval. Cambridge University Press (2009)

    Google Scholar 

  35. Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J.R., Bethard, S., McClosky, D.: The stanford corenlp natural language processing toolkit. In: ACL (2014)

    Google Scholar 

  36. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS (2013)

    Google Scholar 

  37. Nguyen, D.B., Hoffart, J., Theobald, M., Weikum, G.: AIDA-light: high-throughput named-entity disambiguation. In: LDOW (2014)

    Google Scholar 

  38. Oguz, B., et al.: Unik-qa: unified representations of structured and unstructured knowledge for open-domain question answering. arXiv preprint arXiv:2012.14610 (2020)

  39. Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. JMLR (2020)

    Google Scholar 

  40. Rajpurkar, P., Zhang, J., Lopyrev, K., Liang, P.: Squad: 100,000+ questions for machine comprehension of text. arXiv preprint arXiv:1606.05250 (2016)

  41. Riedel, S., Yao, L., McCallum, A., Marlin, B.M.: Relation extraction with matrix factorization and universal schemas. In: NAACL-HLT (2013)

    Google Scholar 

  42. Savenkov, D., Agichtein, E.: When a knowledge base is not enough: question answering over knowledge bases with external text data. In: SIGIR (2016)

    Google Scholar 

  43. Schweter, S., Akbik, A.: FLERT: document-level features for named entity recognition. CoRR (2020)

    Google Scholar 

  44. Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: WWW (2007)

    Google Scholar 

  45. Sun, H., Bedrax-Weiss, T., Cohen, W.W.: Pullnet: open domain question answering with iterative retrieval on knowledge bases and text. arXiv preprint arXiv:1904.09537 (2019)

  46. Verga, P., Belanger, D., Strubell, E., Roth, B., McCallum, A.: Multilingual relation extraction using compositional universal schema. arXiv preprint arXiv:1511.06396 (2015)

  47. Vrandečić, D., Krötzsch, M.: Wikidata: a free collaborative knowledgebase. CACM (2014)

    Google Scholar 

  48. Wang, S., et al.: R 3: Reinforced ranker-reader for open-domain question answering. In: AAAI (2018)

    Google Scholar 

  49. Weikum, G., Dong, X.L., Razniewski, S., Suchanek, F., et al.: Machine knowledge: Creation and curation of comprehensive knowledge bases. Found, Trends Databases (2021)

    Google Scholar 

  50. Xin, R.S., Crankshaw, D., Dave, A., Gonzalez, J.E., Franklin, M.J., Stoica, I.: GraphX: Unifying data-parallel and graph-parallel analytics. CoRR (2014)

    Google Scholar 

  51. Yao, X., Van Durme, B.: Information extraction over structured data: Question answering with freebase. In: ACL (2014)

    Google Scholar 

  52. Yasunaga, M., Ren, H., Bosselut, A., Liang, P., Leskovec, J.: Qa-gnn: reasoning with language models and knowledge graphs for question answering. arXiv preprint arXiv:2104.06378 (2021)

  53. Yu, D., et al.: Kg-fid: infusing knowledge graph in fusion-in-decoder for open-domain question answering. arXiv preprint arXiv:2110.04330 (2021)

  54. Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. In: HotCloud (2010)

    Google Scholar 

  55. Zaharia, M., et al.: Apache Spark: a unified engine for big data processing. CACM (2016)

    Google Scholar 

  56. Zhang, L., et al.: A survey on complex factual question answering. AI Open (2023)

    Google Scholar 

  57. Zhu, S., Cheng, X., Su, S.: Knowledge-based question answering by tree-to-sequence learning. Neurocomputing (2020)

    Google Scholar 

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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

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  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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