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
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
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.
KW - Knowledge graph
KW - Knowledge graph embedding
KW - Natural language question answering
UR - https://www.scopus.com/pages/publications/85065549868
U2 - 10.1007/978-3-030-18576-3_39
DO - 10.1007/978-3-030-18576-3_39
M3 - 会议稿件
AN - SCOPUS:85065549868
SN - 9783030185756
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 659
EP - 675
BT - Database Systems for Advanced Applications - 24th International Conference, DASFAA 2019, Proceedings
A2 - Natwichai, Juggapong
A2 - Yang, Jun
A2 - Li, Guoliang
A2 - Gama, Joao
A2 - Tong, Yongxin
PB - Springer Verlag
T2 - 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019
Y2 - 22 April 2019 through 25 April 2019
ER -