A State-transition Framework to Answer Complex Questions over Knowledge Base

Sen Hu, Lei Zou, Xinbo Zhang


Abstract
Although natural language question answering over knowledge graphs have been studied in the literature, existing methods have some limitations in answering complex questions. To address that, in this paper, we propose a State Transition-based approach to translate a complex natural language question N to a semantic query graph (SQG), which is used to match the underlying knowledge graph to find the answers to question N. In order to generate SQG, we propose four primitive operations (expand, fold, connect and merge) and a learning-based state transition approach. Extensive experiments on several benchmarks (such as QALD, WebQuestions and ComplexQuestions) with two knowledge bases (DBpedia and Freebase) confirm the superiority of our approach compared with state-of-the-arts.
Anthology ID:
D18-1234
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2098–2108
Language:
URL:
https://aclanthology.org/D18-1234
DOI:
10.18653/v1/D18-1234
Bibkey:
Cite (ACL):
Sen Hu, Lei Zou, and Xinbo Zhang. 2018. A State-transition Framework to Answer Complex Questions over Knowledge Base. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2098–2108, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
A State-transition Framework to Answer Complex Questions over Knowledge Base (Hu et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1234.pdf
Attachment:
 D18-1234.Attachment.pdf
Data
WebQuestions