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Enabling Efficient Question Answering over Hundreds of Linked Datasets

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Information Search, Integration, and Personalization (ISIP 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1197))

Abstract

In this paper we introduce an approach, called LODQA, for open domain Question Answering over Linked Open Data. We confine ourselves to three kinds of questions: factoid, confirmation, and definition questions. By using LODQA it is feasible to answer questions over 400 millions of entities of any domain without using any training data, since we exploit simultaneously 400 Linked datasets. In particular, we exploit the services of LODsyndesis, a suite of services (based on semantics-aware indexes) which supports cross-dataset reasoning over hundreds of Linked datasets and 2 billion triples. The proposed Question Answering process follows an information extraction approach and comprises several steps including question cleaning, heuristic based question type identification, entity recognition, linking and disambiguation using Linked Data-based methods and pure NLP methods (specifically DBpedia Spotlight and Stanford CoreNLP), WordNet-based question expansion for tackling the lexical gap (between the input question and the underlying sources), and triple scoring for producing the final answer. We discuss the benefits of this approach in terms of answerable questions and answer verification, and we investigate, through experimental results, how the aforementioned steps of the process affect the effectiveness and the efficiency of question answering.

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Notes

  1. 1.

    https://github.com/extjwnl/extjwnl.

  2. 2.

    http://research.fb.com/downloads/babi/.

  3. 3.

    http://islcatalog.ics.forth.gr/tr/dataset/simplequestions-v2-1000-questions.

References

  1. Abujabal, A., Yahya, M., Riedewald, M., Weikum, G.: Automated template generation for question answering over knowledge graphs. In: Proceedings of the 26th International Conference on World Wide Web, pp. 1191–1200. International World Wide Web Conferences Steering Committee (2017)

    Google Scholar 

  2. Affolter, K., Stockinger, K., Bernstein, A.: A comparative survey of recent natural language interfaces for databases. arXiv preprint arXiv:1906.08990 (2019)

  3. Bast, H., Haussmann, E.: More accurate question answering on freebase. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 1431–1440. ACM (2015)

    Google Scholar 

  4. Berant, J., Liang, P.: Imitation learning of agenda-based semantic parsers. Trans. Assoc. Comput. Linguist. 3, 545–558 (2015)

    Article  Google Scholar 

  5. Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1247–1250. ACM (2008)

    Google Scholar 

  6. Bordes, A., Usunier, N., Chopra, S., Weston, J.: Large-scale simple question answering with memory networks. CoRR, abs/1506.02075 (2015)

    Google Scholar 

  7. Diefenbach, D., Singh, K., Maret, P.: WDAqua-core1: a question answering service for RDF knowledge bases. In: Companion of the The Web Conference 2018, pp. 1087–1091. International World Wide Web Conferences Steering Committee (2018)

    Google Scholar 

  8. Dimitrakis, E., Sgontzos, K., Tzitzikas, Y.: A survey on question answering systems over linked data and documents. J. Intell. Inf. Syst., 1–27 (2019). https://doi.org/10.1007/s10844-019-00584-7

  9. Finkel, J.R., Grenager, T., Manning, C.: Incorporating non-local information into information extraction systems by Gibbs sampling. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 363–370. Association for Computational Linguistics (2005)

    Google Scholar 

  10. Hakimov, S., Jebbara, S., Cimiano, P.: AMUSE: multilingual semantic parsing for question answering over linked data. In: d’Amato, C., et al. (eds.) ISWC 2017. LNCS, vol. 10587, pp. 329–346. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68288-4_20

    Chapter  Google Scholar 

  11. Höffner, K., Walter, S., Marx, E., Usbeck, R., Lehmann, J., Ngonga Ngomo, A.-C.: Survey on challenges of question answering in the semantic web. Seman. Web 8(6), 895–920 (2017)

    Article  Google Scholar 

  12. Lehmann, J., et al.: DBpedia-a large-scale, multilingual knowledge base extracted from Wikipedia. Seman. Web 6(2), 167–195 (2015)

    Article  Google Scholar 

  13. Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J.R., Bethard, S., McClosky, D.: The stanford coreNLP natural language processing toolkit. In: ACL (System Demonstrations), pp. 55–60 (2014)

    Google Scholar 

  14. Mendes, P.N., Jakob, M., García-Silva, A., Bizer, C.: DBpedia spotlight: shedding light on the web of documents. In: Proceedings of the 7th International Conference on Semantic Systems, pp. 1–8. ACM (2011)

    Google Scholar 

  15. Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)

    Article  Google Scholar 

  16. Mishra, A., Jain, S.K.: A survey on question answering systems with classification. J. King Saud Univ. Comput. Inf. Sci. 28(3), 345–361 (2016)

    Google Scholar 

  17. Mountantonakis, M., Tzitzikas, Y.: High performance methods for linked open data connectivity analytics. Information 9(6), 134 (2018)

    Article  Google Scholar 

  18. Mountantonakis, M., Tzitzikas, Y.: LODsyndesis: global scale knowledge services. Heritage 1(2), 335–348 (2018)

    Article  Google Scholar 

  19. Mountantonakis, M., Tzitzikas, Y.: Large scale semantic integration of linked data: a survey. ACM Comput. Surv. (CSUR) 52(5), 103 (2019)

    Article  Google Scholar 

  20. Papangelis, A., Papadakos, P., Stylianou, Y., Tzitzikas, Y.: Spoken dialogue for information navigation. In: Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue, pp. 229–234 (2018)

    Google Scholar 

  21. Patra, B.: A survey of community question answering. CoRR, abs/1705.04009 (2017)

    Google Scholar 

  22. Radoev, N., Tremblay, M., Gagnon, M., Zouaq, A.: Answering natural language questions on RDF knowledge base in French. In: 7th Open Challenge in Question Answering over Linked Data (QALD 2017), Portoroz, Slovenia (2017)

    Google Scholar 

  23. Reddy, S., et al.: Transforming dependency structures to logical forms for semantic parsing. Trans. Assoc. Comput. Linguist. 4, 127–140 (2016)

    Article  Google Scholar 

  24. Rodrigo, A., Peñas, A.: A study about the future evaluation of question-answering systems. Knowl. Based Syst. 137, 83–93 (2017)

    Article  Google Scholar 

  25. Shekarpour, S., Marx, E., Ngomo, A.-C.N., Auer, S.: SINA: semantic interpretation of user queries for question answering on interlinked data. Web Seman. Sci. Serv. Agents World Wide Web 30, 39–51 (2015)

    Article  Google Scholar 

  26. Stockinger, K.: The rise of natural language interfaces to databases. In: ACM SIGMOD Blog (2019)

    Google Scholar 

  27. Tzitzikas, Y., Manolis, N., Papadakos, P.: Faceted exploration of RDF/S datasets: a survey. J. Intell. Inf. Syst. 48, 1–36 (2016)

    Google Scholar 

  28. Wang, M.: A survey of answer extraction techniques in factoid question answering. In: Computational Linguistics, vol. 1, no. 1 (2006)

    Google Scholar 

  29. Yao, X., Berant, J., Van Durme, B.: Freebase QA: information extraction or semantic parsing. In: Proceedings of ACL (2014)

    Google Scholar 

  30. Yavuz, S., Gur, I., Su, Y., Srivatsa, M., Yan, X.: Improving semantic parsing via answer type inference. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 149–159 (2016)

    Google Scholar 

  31. Yih, W.-T., Chang, M.-W., He, X., Gao, J.: Semantic parsing via staged query graph generation: question answering with knowledge base. In: Proceedings of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, vol. 1, pp. 1321–1331 (2015)

    Google Scholar 

  32. Zhang, Y., He, S., Liu, K., Zhao, J.: A joint model for question answering over multiple knowledge bases. In: AAAI, pp. 3094–3100 (2016)

    Google Scholar 

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Acknowledgements

The research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) and the General Secretariat for Research and Technology (GSRT), under the HFRI PhD Fellowship grant (GA. No. 166).

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Authors and Affiliations

  1. Institute of Computer Science, FORTH, Heraklion, Greece

    Eleftherios Dimitrakis, Konstantinos Sgontzos, Michalis Mountantonakis & Yannis Tzitzikas

  2. Computer Science Department, University of Crete, Heraklion, Greece

    Eleftherios Dimitrakis, Konstantinos Sgontzos, Michalis Mountantonakis & Yannis Tzitzikas

Authors
  1. Eleftherios Dimitrakis
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  2. Konstantinos Sgontzos
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  3. Michalis Mountantonakis
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  4. Yannis Tzitzikas
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Corresponding author

Correspondence to Michalis Mountantonakis .

Editor information

Editors and Affiliations

  1. Foundation for Research and Technology Hellas, Heraklion, Greece

    Giorgos Flouris

  2. University of Cergy-Pontoise, Cergy Pontoise, France

    Dominique Laurent

  3. Foundation for Research and Technology Hellas, Heraklion, Greece

    Dimitris Plexousakis

  4. University of Paris-Sud, Orsay, France

    Nicolas Spyratos

  5. Hokkaido University, Sapporo, Japan

    Yuzuru Tanaka

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Dimitrakis, E., Sgontzos, K., Mountantonakis, M., Tzitzikas, Y. (2020). Enabling Efficient Question Answering over Hundreds of Linked Datasets. In: Flouris, G., Laurent, D., Plexousakis, D., Spyratos, N., Tanaka, Y. (eds) Information Search, Integration, and Personalization. ISIP 2019. Communications in Computer and Information Science, vol 1197. Springer, Cham. https://doi.org/10.1007/978-3-030-44900-1_1

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  • DOI: https://doi.org/10.1007/978-3-030-44900-1_1

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