Advertisement

Complex Query Augmentation for Question Answering over Knowledge Graphs

  • Abdelrahman Abdelkawi
  • Hamid ZafarEmail author
  • Maria Maleshkova
  • Jens Lehmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11877)

Abstract

Question answering systems have often a pipeline architecture that consists of multiple components. A key component in the pipeline is the query generator, which aims to generate a formal query that corresponds to the input natural language question. Even if the linked entities and relations to an underlying knowledge graph are given, finding the corresponding query that captures the true intention of the input question still remains a challenging task, due to the complexity of sentence structure or the features that need to be extracted. In this work, we focus on the query generation component and introduce techniques to support a wider range of questions that are currently less represented in the community of question answering.

Keywords

Question answering Knowledge graphs Query augmentation 

Notes

Acknowledgments

This research was supported by the European Union H2020 project CLEOPATRA (ITN, GA. 812997) as well as by the German Federal Ministry of Education and Research (BMBF) funding for the project SOLIDE (no. 13N14456).

References

  1. 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. 2.
    Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Cudré-Mauroux, P., et al. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-76298-0_52CrossRefGoogle Scholar
  3. 3.
    Berant, J., Chou, A., Frostig, R., Liang, P.: Semantic parsing on freebase from question-answer pairs. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1533–1544 (2013)Google Scholar
  4. 4.
    Berant, J., Liang, P.: Semantic parsing via paraphrasing. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1415–1425 (2014)Google Scholar
  5. 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. 6.
    Bordes, A., Chopra, S., Weston, J.: Question answering with subgraph embeddings. arXiv preprint arXiv:1406.3676 (2014)
  7. 7.
    Bordes, A., Usunier, N., Chopra, S., Weston, J.: Large-scale simple question answering with memory networks. arXiv preprint arXiv:1506.02075 (2015)
  8. 8.
    Diefenbach, D., Both, A., Singh, K., Maret, P.: Towards a question answering system over the semantic web. Semant. Web (Preprint) 1–19 (2018)Google Scholar
  9. 9.
    Diefenbach, D., Lopez, V., Singh, K., Maret, P.: Core techniques of question answering systems over knowledge bases: a survey. Knowl. Inf. Syst. 55(3), 529–569 (2018)CrossRefGoogle Scholar
  10. 10.
    Dubey, M., Banerjee, D., Abdelkawi, A., Lehmann, J.: Lc-quad 2.0: a large dataset for complex question answering over Wikidata and dbpedia. In: Proceedings of the 18th International Semantic Web Conference (ISWC). Springer (2019)Google Scholar
  11. 11.
    Dubey, M., Dasgupta, S., Sharma, A., Höffner, K., Lehmann, J.: AskNow: a framework for natural language query formalization in SPARQL. In: Sack, H., Blomqvist, E., d’Aquin, M., Ghidini, C., Ponzetto, S.P., Lange, C. (eds.) ESWC 2016. LNCS, vol. 9678, pp. 300–316. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-34129-3_19CrossRefGoogle Scholar
  12. 12.
    Hakimov, S., Unger, C., Walter, S., Cimiano, P.: Applying semantic parsing to question answering over linked data: addressing the lexical gap. In: Biemann, C., Handschuh, S., Freitas, A., Meziane, F., Métais, E. (eds.) NLDB 2015. LNCS, vol. 9103, pp. 103–109. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-19581-0_8CrossRefGoogle Scholar
  13. 13.
    Hamon, T., Grabar, N., Mougin, F., Thiessard, F.: Description of the POMELO system for the task 2 of QALD-2014. In: CLEF (Working Notes), pp. 1212–1223 (2014)Google Scholar
  14. 14.
    Höffner, K., Walter, S., Marx, E., Usbeck, R., Lehmann, J., Ngonga Ngomo, A.C.: Survey on challenges of question answering in the semantic web. Semant. Web 8(6), 895–920 (2017)CrossRefGoogle Scholar
  15. 15.
    Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: International Conference on Machine Learning, pp. 957–966 (2015)Google Scholar
  16. 16.
    Lehmann, J., et al.: DBpedia - a large-scale, multilingual knowledge base extracted from wikipedia. Semant. Web J. 6(2), 167–195 (2015)CrossRefGoogle Scholar
  17. 17.
    Lindberg, D.A., Humphreys, B.L., McCray, A.T.: The unified medical language system. Yearb. Med. Inf. 2(01), 41–51 (1993)CrossRefGoogle Scholar
  18. 18.
    Lukovnikov, D., Fischer, A., Lehmann, J., Auer, S.: Neural network-based question answering over knowledge graphs on word and character level. In: Proceedings of the 26th International Conference on World Wide Web, pp. 1211–1220. International World Wide Web Conferences Steering Committee (2017)Google Scholar
  19. 19.
    Maheshwari, G., Trivedi, P., Lukovnikov, D., Chakraborty, N., Fischer, A., Lehmann, J.: Learning to rank query graphs for complex question answering over knowledge graphs. In: International Semantic Web Conference. Springer (2019)Google Scholar
  20. 20.
    Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  21. 21.
    Ngomo, N.: 9th challenge on question answering over linked data (QALD-9). language 7, 1Google Scholar
  22. 22.
    Chakraborty, N., Lukovnikov, D., Maheshwari, G., Trivedi, P., Lehmann, J., Fischer, A.: Introduction to neural network based approaches for question answering over knowledge graphs (2019)Google Scholar
  23. 23.
    Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)Google Scholar
  24. 24.
    Shekarpour, S., Marx, E., Ngomo, A.C.N., Auer, S.: Sina: Semantic interpretation of user queries for question answering on interlinked data. Web Semant.: Sci. Serv. Agents World Wide Web 30, 39–51 (2015)CrossRefGoogle Scholar
  25. 25.
    Singh, K., et al.: Why reinvent the wheel: Let’s build question answering systems together. In: Proceedings of the 2018 World Wide Web Conference on World Wide Web, pp. 1247–1256. International World Wide Web Conferences Steering Committee (2018)Google Scholar
  26. 26.
    SZ, H., et al.: Casia@ v2: A MLN-based question answering system over linked data (2014)Google Scholar
  27. 27.
    Trivedi, P., Maheshwari, G., Dubey, M., Lehmann, J.: LC-QuAD: a corpus for complex question answering over knowledge graphs. In: d’Amato, C., et al. (eds.) ISWC 2017. LNCS, vol. 10588, pp. 210–218. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-68204-4_22CrossRefGoogle Scholar
  28. 28.
    Unger, C., Bühmann, L., Lehmann, J., Ngonga Ngomo, A.C., Gerber, D., Cimiano, P.: Template-based question answering over RDF data. In: Proceedings of the 21st International Conference on World Wide Web, pp. 639–648. ACM (2012)Google Scholar
  29. 29.
    Walter, S., Unger, C., Cimiano, P., Bär, D.: Evaluation of a layered approach to question answering over linked data. In: Cudré-Mauroux, P., et al. (eds.) ISWC 2012. LNCS, vol. 7650, pp. 362–374. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-35173-0_25CrossRefGoogle Scholar
  30. 30.
    Wick, M.: GeoNames. GeoNames (2006)Google Scholar
  31. 31.
    Yih, S.W.T., Chang, M.W., He, X., Gao, J.: Semantic parsing via staged query graph generation: question answering with knowledge base (2015)Google Scholar
  32. 32.
    Yin, W., Yu, M., Xiang, B., Zhou, B., Schütze, H.: Simple question answering by attentive convolutional neural network. arXiv preprint arXiv:1606.03391 (2016)
  33. 33.
    Zafar, H., Napolitano, G., Lehmann, J.: Formal query generation for question answering over knowledge bases. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 714–728. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-93417-4_46CrossRefGoogle Scholar
  34. 34.
    Zettlemoyer, L.S., Collins, M.: Learning to map sentences to logical form: structured classification with probabilistic categorial grammars. arXiv preprint arXiv:1207.1420 (2012)

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Abdelrahman Abdelkawi
    • 1
  • Hamid Zafar
    • 2
    Email author
  • Maria Maleshkova
    • 2
  • Jens Lehmann
    • 2
    • 3
  1. 1.Computer Science InstituteRWTH Aachen UniversityAachenGermany
  2. 2.Computer Science InstituteUniversity of BonnBonnGermany
  3. 3.Fraunhofer IAISDresdenGermany

Personalised recommendations