Leveraging knowledge graph embeddings for natural language question answering

Ruijie Wang; Meng Wang; Jun Liu; Weitong Chen; Michael Cochez; Stefan Decker

doi:10.1007/978-3-030-18576-3_39

Leveraging knowledge graph embeddings for natural language question answering

Ruijie Wang, Meng Wang^*, Jun Liu, Weitong Chen, Michael Cochez, Stefan Decker

^*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

Abstract

A promising pathway for natural language question answering over knowledge graphs (KG-QA) is to translate natural language questions into graph-structured queries. During the translation, a vital process is to map entity/relation phrases of natural language questions to the vertices/edges of underlying knowledge graphs which can be used to construct target graph-structured queries. However, due to linguistic flexibility and ambiguity of natural language, the mapping process is challenging and has been a bottleneck of KG-QA models. In this paper, we propose a novel framework, called KemQA, which stands on recent advances in relation phrase dictionaries and knowledge graph embedding techniques to address the mapping problem and construct graph-structured queries of natural language questions. Extensive experiments were conducted on question answering benchmark datasets. The results demonstrate that our framework outperforms state-of-the-art baseline models in terms of effectiveness and efficiency.

Original language	English
Title of host publication	Database Systems for Advanced Applications - 24th International Conference, DASFAA 2019, Proceedings
Editors	Juggapong Natwichai, Jun Yang, Guoliang Li, Joao Gama, Yongxin Tong
Publisher	Springer Verlag
Pages	659-675
Number of pages	17
ISBN (Print)	9783030185756
DOIs	https://doi.org/10.1007/978-3-030-18576-3_39
Publication status	Published - 1 Jan 2019
Externally published	Yes
Event	24th International Conference on Database Systems for Advanced Applications, DASFAA 2019 - Chiang Mai, Thailand Duration: 22 Apr 2019 → 25 Apr 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11446 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	24th International Conference on Database Systems for Advanced Applications, DASFAA 2019
Country/Territory	Thailand
City	Chiang Mai
Period	22/04/19 → 25/04/19

Funding

Acknowledgment. This work was supported by National Key Research and Development Program of China (2018YFB1004500), National Natural Science Foundation of China (61532015, 61532004, 61672419, 61672418, and U1736204), Innovative Research Group of the National Natural Science Foundation of China (61721002), Innovation Research Team of Ministry of Education (IRT 17R86), Project of China Knowledge Centre for Engineering Science and Technology, Teaching Reform Project of XJTU (No. 17ZX044), and China Scholarship Council (No. 201806280450).

Funders	Funder number
National Natural Science Foundation of China	61672418, 61672419, 61721002, 61532004, 61532015, U1736204
Ministry of Education of the People's Republic of China	IRT 17R86
Xi’an Jiaotong University	17ZX044
China Scholarship Council	201806280450
National Key Research and Development Program of China	2018YFB1004500

Keywords

Knowledge graph
Knowledge graph embedding
Natural language question answering

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10.1007/978-3-030-18576-3_39

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Wang, R., Wang, M., Liu, J., Chen, W., Cochez, M., & Decker, S. (2019). Leveraging knowledge graph embeddings for natural language question answering. In J. Natwichai, J. Yang, G. Li, J. Gama, & Y. Tong (Eds.), Database Systems for Advanced Applications - 24th International Conference, DASFAA 2019, Proceedings (pp. 659-675). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11446 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-18576-3_39

Wang, Ruijie ; Wang, Meng ; Liu, Jun et al. / Leveraging knowledge graph embeddings for natural language question answering. Database Systems for Advanced Applications - 24th International Conference, DASFAA 2019, Proceedings. editor / Juggapong Natwichai ; Jun Yang ; Guoliang Li ; Joao Gama ; Yongxin Tong. Springer Verlag, 2019. pp. 659-675 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Leveraging knowledge graph embeddings for natural language question answering",

abstract = "A promising pathway for natural language question answering over knowledge graphs (KG-QA) is to translate natural language questions into graph-structured queries. During the translation, a vital process is to map entity/relation phrases of natural language questions to the vertices/edges of underlying knowledge graphs which can be used to construct target graph-structured queries. However, due to linguistic flexibility and ambiguity of natural language, the mapping process is challenging and has been a bottleneck of KG-QA models. In this paper, we propose a novel framework, called KemQA, which stands on recent advances in relation phrase dictionaries and knowledge graph embedding techniques to address the mapping problem and construct graph-structured queries of natural language questions. Extensive experiments were conducted on question answering benchmark datasets. The results demonstrate that our framework outperforms state-of-the-art baseline models in terms of effectiveness and efficiency.",

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author = "Ruijie Wang and Meng Wang and Jun Liu and Weitong Chen and Michael Cochez and Stefan Decker",

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Wang, R, Wang, M, Liu, J, Chen, W, Cochez, M & Decker, S 2019, Leveraging knowledge graph embeddings for natural language question answering. in J Natwichai, J Yang, G Li, J Gama & Y Tong (eds), Database Systems for Advanced Applications - 24th International Conference, DASFAA 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11446 LNCS, Springer Verlag, pp. 659-675, 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019, Chiang Mai, Thailand, 22/04/19. https://doi.org/10.1007/978-3-030-18576-3_39

Leveraging knowledge graph embeddings for natural language question answering. / Wang, Ruijie; Wang, Meng; Liu, Jun et al.
Database Systems for Advanced Applications - 24th International Conference, DASFAA 2019, Proceedings. ed. / Juggapong Natwichai; Jun Yang; Guoliang Li; Joao Gama; Yongxin Tong. Springer Verlag, 2019. p. 659-675 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11446 LNCS).

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Leveraging knowledge graph embeddings for natural language question answering

AU - Wang, Ruijie

AU - Wang, Meng

AU - Liu, Jun

AU - Chen, Weitong

AU - Cochez, Michael

AU - Decker, Stefan

PY - 2019/1/1

Y1 - 2019/1/1

N2 - A promising pathway for natural language question answering over knowledge graphs (KG-QA) is to translate natural language questions into graph-structured queries. During the translation, a vital process is to map entity/relation phrases of natural language questions to the vertices/edges of underlying knowledge graphs which can be used to construct target graph-structured queries. However, due to linguistic flexibility and ambiguity of natural language, the mapping process is challenging and has been a bottleneck of KG-QA models. In this paper, we propose a novel framework, called KemQA, which stands on recent advances in relation phrase dictionaries and knowledge graph embedding techniques to address the mapping problem and construct graph-structured queries of natural language questions. Extensive experiments were conducted on question answering benchmark datasets. The results demonstrate that our framework outperforms state-of-the-art baseline models in terms of effectiveness and efficiency.

AB - A promising pathway for natural language question answering over knowledge graphs (KG-QA) is to translate natural language questions into graph-structured queries. During the translation, a vital process is to map entity/relation phrases of natural language questions to the vertices/edges of underlying knowledge graphs which can be used to construct target graph-structured queries. However, due to linguistic flexibility and ambiguity of natural language, the mapping process is challenging and has been a bottleneck of KG-QA models. In this paper, we propose a novel framework, called KemQA, which stands on recent advances in relation phrase dictionaries and knowledge graph embedding techniques to address the mapping problem and construct graph-structured queries of natural language questions. Extensive experiments were conducted on question answering benchmark datasets. The results demonstrate that our framework outperforms state-of-the-art baseline models in terms of effectiveness and efficiency.

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KW - Knowledge graph embedding

KW - Natural language question answering

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Y2 - 22 April 2019 through 25 April 2019

ER -

Wang R, Wang M, Liu J, Chen W, Cochez M, Decker S. Leveraging knowledge graph embeddings for natural language question answering. In Natwichai J, Yang J, Li G, Gama J, Tong Y, editors, Database Systems for Advanced Applications - 24th International Conference, DASFAA 2019, Proceedings. Springer Verlag. 2019. p. 659-675. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-18576-3_39

Leveraging knowledge graph embeddings for natural language question answering

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