Semantic Structure Based Query Graph Prediction for Question Answering over Knowledge Graph

Mingchen Li, Shihao Ji


Abstract
Building query graphs from natural language questions is an important step in complex question answering over knowledge graph (Complex KGQA). In general, a question can be correctly answered if its query graph is built correctly and the right answer is then retrieved by issuing the query graph against the KG. Therefore, this paper focuses on query graph generation from natural language questions. Existing approaches for query graph generation ignore the semantic structure of a question, resulting in a large number of noisy query graph candidates that undermine prediction accuracies. In this paper, we define six semantic structures from common questions in KGQA and develop a novel Structure-BERT to predict the semantic structure of a question. By doing so, we can first filter out noisy candidate query graphs by the predicted semantic structures, and then rank the remaining candidates with a BERT-based ranking model. Extensive experiments on two popular benchmarks MetaQA and WebQuestionsSP (WSP) demonstrate the effectiveness of our method as compared to state-of-the-arts.
Anthology ID:
2022.coling-1.135
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
1569–1579
Language:
URL:
https://aclanthology.org/2022.coling-1.135
DOI:
Bibkey:
Cite (ACL):
Mingchen Li and Shihao Ji. 2022. Semantic Structure Based Query Graph Prediction for Question Answering over Knowledge Graph. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1569–1579, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Semantic Structure Based Query Graph Prediction for Question Answering over Knowledge Graph (Li & Ji, COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.135.pdf
Code
 toneli/sskgqa
Data
MetaQAWebQuestionsSP