research-article

Developing a Vietnamese Tourism Question Answering System Using Knowledge Graph and Deep Learning

Authors: Phuc Do, Truong H. V. Phan, Brij B. GuptaAuthors Info & Claims

Transactions on Asian and Low-Resource Language Information Processing, Volume 20, Issue 5

Article No.: 81, Pages 1 - 18

https://doi.org/10.1145/3453651

Published: 30 June 2021 Publication History

Abstract

In recent years, Question Answering (QA) systems have increasingly become very popular in many sectors. This study aims to use a knowledge graph and deep learning to develop a QA system for tourism in Vietnam. First, the QA system replies to a user's question about a place in Vietnam. Then, the QA describes it in detail such as when the place was discovered, why the place's name was called like that, and so on. Finally, the system recommends some related tourist attractions to users. Meanwhile, deep learning is used to solve a simple natural language answer, and a knowledge graph is used to infer a natural language answering list related to entities in the question. The study experiments on a manual dataset collected from Vietnamese tourism websites. As a result, the QA system combining the two above approaches provides more information than other systems have done before. Besides that, the system gets 0.83 F1, 0.87 precision on the test set.

References

[1]

Abu Shawar and Eric Atwell. 2009. Arabic question-answering via instance-based learning from an FAQ corpus. In International Conference on Corpus Linguistics.

[2]

Alberto Garcia-Garcia, Sergio Orts-Escolano, Sergiu Oprea, Victor Villena-Martinez, Pablo Martinez Gonzalez, and Jose Garcia Rodriguez. 2018. A survey on deep learning techniques for image and video semantic segmentation. Appl. Soft Comput. 70 (2018), 41–56.

[3]

Andreas Kamilaris, Francesc Xavier Prenafeta Boldú. 2018. Deep learning in agriculture: A survey. Comput. Electron. Agric. 147 (2018), 70--90.

[4]

Annervaz K. M., Somnath Basu Roy Chowdhury, and Ambedkar Dukkipati. 2018. Learning beyond datasets: Knowledge graph augmented neural networks for natural language processing. In Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 313–322.

[5]

Ben Hixon, Peter Clark, and Hannaneh Hajishirzi. 2015. Learning knowledge graphs for question answering through conversational dialog. In Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technology. 851–861.

[6]

Crissman Loomis, Tobias Pfeiffer, Niboshi and Keisuke Umezama. 2019. Write a Sequence to Sequence (seq2seq) Model. Retrieved from https://docs.chainer.org/en/stable/examples/seq2seq.html.

[7]

Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017. Reading Wikipedia to answer open-domain questions. arXiv:1704.00051.

[8]

Djallel Bouneffouf. 2013. DRARS, a dynamic risk-aware recommender system. Comput. Lang. Institut National des Télécommunications.

[9]

Eric Greenstein and Daniel Penner. 2015. Japanese-to-English Machine Translation Using Recurrent Neural Networks. https://www.aclweb.org/anthology/Y18-3014.pdf.

[10]

Erion Cano and Maurizio Morisio. 2019. Hybrid recommender systems: A systematic literature review. arXiv:1901.03888.

[11]

Francesco Ricci. 2011. Mobile recommender systems. Inf. Technol. Tour. 12, 3 (2011), 205–231.

[12]

G. Rohit, Ekta Gautam Dharamshi, and Natarajan Subramanyam. 2018. Approaches to question answering using LSTM and memory networks. In Advances in Intelligent Systems and Computing book series (AISC), vol. 816.

[13]

Gaurav Maheshwari, Priyansh Trivedi, Denis Lukovnikov, Nilesh Chakraborty, Asja Fischer, and Jens Lehmann. 2019. Learning to rank query graphs for complex question answering over knowledge graphs. In 18th International Semantic Web Conference. 26–30.

[14]

Harshil Shah and David Barber. 2018. Generative neural machine translation. In 32nd Conference on Neural Information Processing Systems. Curran Associates Inc.

Digital Library

[15]

Hongyu Li, Chenyan Xiong, and Jamie Callan. 2017. Natural language supported relation matching for question answering with knowledge graphs. In 1st Workshop on Knowledge Graphs and Semantics for Text Retrieval and Analysis (KG4IR’17).

[16]

Huu-Thanh Duong and Bao-Quoc Ho. 2014. A Vietnamese question answering system in Vietnam's legal. In 13th IFIP International Conference on Computer Information Systems and Industrial Management. 186–197.

[17]

Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to sequence with neural networks. In International Conference on Advances in Neural Information Processing Systems.

[18]

Jamie Castro and Adrián Soto. 2017. A comparison between cypher and conjunctive queries. In 11th Alberto Mendelzon International Workshop on Foundations of Data Management and the Web Montevideo, ACM Woodstock Conference.

[19]

Janhavi R. Chaudhary and Ankit C. Patel. 2018. Bilingual machine translation using RNN based deep learning. Int. J. Scientific Res. Sci. Eng. Technol. 4, 4 (2018), 1480–1484.

[20]

Kleanthi Lakiotaki, Nikolaos F. Matsatsinis, and Alexis Tsoukiàs. 2011. Multi-criteria user modeling in recommender systems. IEEE Intelligent Systems 26, 2 (2011), 64--76.

Digital Library

[21]

M. M. Sakre, M. M. Kouta, and A. M. N. Allam. 2009. Automated construction of Arabic-English parallel corpus. J. Adv. Comput. Sci. 3, 1, Article 5 (2009), 5.

[22]

Mack, David. 2018. Answering English Questions Using Knowledge Graphs and Sequence Translation. Retrieved from https://medium.com/octavian-ai/answering-english-questions-using-knowledge-graphs-and-sequence-translation-2acbaa35a21d.

[23]

Mehrbakhsh Nilashi, Karamollah Bagherifard, Othman Ibrahim, Hamid Alizadeh, Lasisi Ayodele Nojeem, and Nazanin Roozegar. 2013. Collaborative filtering recommender systems. Res. J. Appl. Sci. Eng. Technol. 5, 16 (2013), 4168–4182.

[24]

Mohnish Dubey, Debayan Banerjee, Debanjan Chaudhuri, and Jens Lehmann. 2018. EARL: Joint entity and relation linking for question answering over knowledge graphs. In International Semantic Web Conference.

Digital Library

[25]

Nadime Francis, Alastair Green, Paolo Guagliardo, Leonid Libkin, Tobias Lindaaker, Mats Rydberg, and Andrés Taylor. 2018. Cypher: An evolving query language for property graphs. ACM Special Interest Group on Management of Data. https://hal.archives-ouvertes.fr/hal-01803524.

[26]

P. Banerjee and H. Han. 2007. Drexel at TREC 2007: Question answering. In 16th Text Retrieval Conference (TREC’07).

[27]

Pasquale Lops, Marco de Gemmis, and Giovanni Semeraro. 2011. Content-based recommender systems: State of the art and trends. In Recommender Systems Handbook. Springer, Boston, MA, 73–105.

[28]

Peng Qi, Xiaowen Lin, Leo Mehr, Zijian Wang, and Christopher D. Manning. 2019. Answering complex open-domain questions through iterative query generation. Hong Kong. arXiv:1910.07000.

[29]

Preena M. P. Shibily. 2019. Question answering using deep learning. In International Conference on Systems, Energy & Environment (ICSEE’19).

[30]

Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, and Qing He. 2020. A survey on knowledge graph-based recommender systems. IEEE Transactions on Knowledge and Data Engineering. https://doi.ieeecomputersociety.org/10.1109/TKDE.2020.3028705

[31]

Renzo Angles, Marcelo Arenas, Pablo Barceló, Aidan Hogan, Juan Reutter, and Domagoj Vrgoc. 2017. Foundations of modern query languages for graph databases. ACM Comput. Surv. 50, 5, Article 68 (2017), 1--40.

[32]

Rik Van Bruggen. 2014. Learning Neo4j. Packt Publishing.

[33]

Robin Burke, Alexander Felfernig, and Mehmet H. Göker. 2011. Recommender systems: An overview. AI Mag. 32, 3 (2011), 13--18.

Digital Library

[34]

Rukhsana Thaker and Ajay Goel. 2015. QUrdPro: Query processing system for Urdu Language. Int. J. Eng. Res. Applic. 5, 6 (2015), 106–108.

[35]

Sanda M. Harabagiu, Marius A. Pasca, and Steven J. Maiorano. 2000. Experiments with open-domain textual question answering. In International Conference on Computational Linguistics.

[36]

Sepp Hochreiter and Jurgen Schmidhuber. 1997. Long short-term memory. Neural Comput. 9, 8 (1997), 1735–1780.

Digital Library

[37]

Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, Philip S. Yu. 2020. A survey on knowledge graphs: Representation, acquisition, and applications. arXiv:2002.00388 [cs.CL].

[38]

Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM Comput. Surv. 52, 1, Article 5 (2019), 1--38.

[39]

Tran Trung, Anh-Duc Hoang, Trung Tien Nguyen, Viet-Hung Dinh, Yen-Chi Nguyen, and Hiep-Hung Pham. 2020. Dataset of Vietnamese student's learning habits during COVID-19. Data in Brief. 30 (2020), 105682.

[40]

Vanessa Lopez, Pierpaolo Tommasi, Spyros Kotoulas, and Jiewen Wu. 2016. QuerioDALI: Question answering over dynamic and linked knowledge graphs. In 15th International Semantic Web Conference.

[41]

Weigo Zheng, Jeffrey Xu Yu, Lei Zou, and Hong Cheng. 2018. Question answering over knowledge graphs: Question understanding via template de-composition. VLDB Endow. 11, 11 (2018), 1373–1386.

Digital Library

[42]

Weiping Song, Zhijian Duan, Ziqing Yang, Hao Zhu, Ming Zhang, and Jian Tang. 2019. Explainable knowledge graph-based recommendation via deep reinforcement learning. arXiv:1906.09506 [cs.IR].

[43]

Xiao Huang, Jingyuan Zhang, Dingcheng Li, and Ping Li. 2019. Knowledge graph embedding based question answering. In 12th ACM International Conference on Web Search and Data Mining (WSDM’19).

Digital Library

[44]

Xiaolu Lu, Soumajit Pramanik, Rishiraj Saha Roy, Abdalghani Abujabal, Yafang Wang, and Gerhard Weikum. 2019. Answering complex questions by joining multi-document evidence with qasi knowledge graphs. In 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval.

Digital Library

[45]

Yashvardhan Sharmaa and Sahil Gupta. 2018. Deep learning approaches for question answering system. In International Conference on Computational Intelligence and Data Science.

Digital Library

[46]

Yonghui Wu et al. 2016. Google's neural machine translation system: Bridging the gap between human and machine translation. arXiv:1609.08144 [cs.CL].

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Index Terms

Developing a Vietnamese Tourism Question Answering System Using Knowledge Graph and Deep Learning
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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Published In

cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 20, Issue 5

September 2021

320 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/3467024

Editor:
Imed Zitouni
Google, USA

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Copyright © 2021 Association for Computing Machinery.

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Association for Computing Machinery

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Publication History

Published: 30 June 2021

Accepted: 01 March 2021

Revised: 01 November 2020

Received: 01 January 2020

Published in TALLIP Volume 20, Issue 5

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Vietnam National University Ho Chi Minh City (VNU-HCMC)

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Cited By

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https://dl.acm.org/doi/10.4018/IJSWIS.323921
Gaurav AHu BChoi CAlmomani A(2022)Evaluation and Comparative Analysis of Semantic Web-Based Strategies for Enhancing Educational System DevelopmentInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.30289518:1(1-14)Online publication date: 6-May-2022
https://dl.acm.org/doi/10.4018/IJSWIS.302895
Ma Y(2022)QA Learning System-Based English Listening and Speaking Ability Improvement StrategyMobile Information Systems10.1155/2022/75607142022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/7560714
Qiao LQu RLiu SZhang YWang HLiu B(2022)Roadmap on Industrial Knowledge System for Data-Oriented Intelligent Operation and Maintenance in Chinese Power IndustryProceedings of the 2022 11th International Conference on Networks, Communication and Computing10.1145/3579895.3579936(267-276)Online publication date: 9-Dec-2022
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Lei LYuan GZhang TYu H(2022)Low-Power Feature-Attention Chinese Keyword Spotting Framework with Distillation LearningACM Transactions on Asian and Low-Resource Language Information Processing10.1145/355800222:2(1-14)Online publication date: 27-Dec-2022
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Van Nguyen KVan Huynh TNguyen DNguyen ANguyen N(2022)New Vietnamese Corpus for Machine Reading Comprehension of Health News ArticlesACM Transactions on Asian and Low-Resource Language Information Processing10.1145/352763121:5(1-28)Online publication date: 23-Sep-2022
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Nassiri KAkhloufi M(2022)Transformer models used for text-based question answering systemsApplied Intelligence10.1007/s10489-022-04052-853:9(10602-10635)Online publication date: 20-Aug-2022
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Phan TDo P(2022)NER2QUES: combining named entity recognition and sequence to sequence to automatically generating Vietnamese questionsNeural Computing and Applications10.1007/s00521-021-06477-734:2(1593-1612)Online publication date: 1-Jan-2022
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