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
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 1569–1579
- Language:
- URL:
- https://preview.aclanthology.org/remove-affiliations/2022.coling-1.135/
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/remove-affiliations/2022.coling-1.135.pdf
- Code
- toneli/sskgqa
- Data
- MetaQA, WebQuestionsSP