Constructing a Hybrid Automatic Q&A System Integrating Knowledge Graph and Information Retrieval Technologies

  • Yang LiuEmail author
  • Bin Xu
  • Yuji Yang
  • Tonglee Chung
  • Peng Zhang
Conference paper
Part of the Lecture Notes in Educational Technology book series (LNET)


Question-answering (QA) system provides a friendly way for human-computer interaction, which has become an important research direction of smart learning. It provides an easy and individual way for the learner to acquire knowledge. This paper focuses on K-12 education and constructs a hybrid automatic question-answering system which integrates Knowledge Based Question Answering (KB-QA) and Information Retrieval-based Question Answering (IR-QA). The system is built based on Chinese textbooks and a Chinese K-12 knowledge graph ( Our QA system covers 9 subjects in K-12 education field, including mathematics, Chinese, geography, history, etc. We evaluate our system on more than 9,000 questions, and achieve average accuracy over 70%. The system could provide effective assistance for teachers’ teaching and students’ learning.


Smart Educatio Knowledge Base Question Answering 


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This work is partly supported by National Engineering Laboratory for Cyberlearning and Intelligent Technology. Beijing Key Lab of Networked Multimedia also supports our research work.


  1. [1] Zhu, Z.T., He,B.: 智慧教育:教育信息化的新境界(Smart education: The new realm of education informatization). E-education Research. 12, 7–15(2012)Google Scholar
  2. [2] Zadeh, L.A.: The role of fuzzy logic in the management of uncertainty in expert systems. Fuzzy Sets & Systems. 11(1), 197–198(1983)Google Scholar
  3. [3] Andrenucci, A., Sneiders, E.: Automated question answering: review of the main approaches. In: International Conference on Information Technology and Applications, IEEE,pp. 514–519 (2005)Google Scholar
  4. [4] High, Rob.: The era of cognitive systems: An inside look at IBM Watson and how it works.IBM Corporation, Redbooks(2012).Google Scholar
  5. [6] Berant, Jonathan.: Semantic parsing on freebase from question-answer pairs. Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing(2013)Google Scholar
  6. [7] Xu, K.: Question answering via phrasal semantic parsing. In: International Conference of the Cross-Language Evaluation Forum for European Languages. Springer, Cham(2015)Google Scholar
  7. [8] Yih, Scott.Wen-tau.:Semantic parsing via staged query graph generation: Question answering with knowledge base (2015)Google Scholar
  8. [9] Xu. K.: Question answering on freebase via relation extraction and textual evidence. arXiv preprint arXiv:1603.00957 (2016)
  9. [10] Bast. H, Haussmann. E.: More accurate question answering on freebase. Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, ACM (2015).Google Scholar
  10. [11] Bordes. A, Chopra. S, Weston. J.: Question answering with subgraph embeddings. arXiv preprint arXiv:1406.3676 (2014)
  11. [12] Yih, Scott Wen-tau, et al. Semantic parsing via staged query graph generation: Question answering with knowledge base. (2015)Google Scholar
  12. [13] What is “AI-MATHS” and “Aidam”,
  13. [14] Robot beat 80% of students on University of Tokyo entrance exam,
  14. [16] Abujabal. A, Yahya. M, Riedewald. M.: Automated template generation for question answering over knowledge graphs. Proceedings of the 26th international conference on world wide web. International World Wide Web Conferences Steering Committee (2017)Google Scholar
  15. [17] Tous. R, Delgado. J.: A vector space model for semantic similarity calculation and OWL ontology alignment. In: International Conference on Database and Expert Systems Applications, pp.207–216. Springer, Berlin, Heidelberg(2006)Google Scholar
  16. [18] Gormley.C, Zachary.T.: Elasticsearch: The Definitive Guide: A Distributed Real-Time Search and Analytics Engine. O’Reilly Media, Inc(2015)Google Scholar
  17. [19] Mohit.B.: Named entity recognition. In: Natural language processing of semitic languages, pp: 221–245. Springer, Berlin, Heidelberg(2014)Google Scholar
  18. [20] Shen.W, W.J.Y.: Entity linking with a knowledge base: Issues, techniques, and solutions. IEEE Transactions on Knowledge and Data Engineering(2015)Google Scholar
  19. [21] Resource Description Framework (RDF),
  20. [23] Wang, Z.G., W. Hamza., Radu. F.: Bilateral multi-perspective matching for natural language sentences. arXiv preprint arXiv:1702.03814 (2017)
  21. [24] K-12 education knowledge graph,
  22. [25] Dong, L., et al. Question Answering over Freebase with Multi-Column Convolutional Neural Networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, vol. 1, pp. 260–269 (2015)Google Scholar
  23. [26] Tan, M., Cicero, D.: Lstm-Based Deep Learning Models For Nonfactoid answer Selection. In: Computer Science(2015)Google Scholar
  24. [27] Mohammed, Salman, Peng. S, Jimmy L.: Strong Baselines for Simple Question Answering over Knowledge Graphs with and without Neural Networks. arXiv: 1712.01969 (2017).Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Yang Liu
    • 1
    Email author
  • Bin Xu
    • 1
  • Yuji Yang
    • 1
  • Tonglee Chung
    • 1
  • Peng Zhang
    • 1
  1. 1.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

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