Abstract
The incompleteness of knowledge base (KB) is a vital factor limiting the performance of question answering (QA). This paper proposes a novel QA method by leveraging text information to enhance the incomplete KB. The model enriches the entity representation through semantic information contained in the text, and employs graph convolutional networks to update the entity status. Furthermore, to exploit the latent structural information of text, we treat the text as hyperedges connecting entities among it to complement the deficient relations in KB, and hypergraph convolutional networks are further applied to reason on the hypergraph-formed text. Extensive experiments on the WebQuestionsSP benchmark with different KB settings prove the effectiveness of our model.- Anthology ID:
- 2020.findings-emnlp.133
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1475–1481
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.133
- DOI:
- 10.18653/v1/2020.findings-emnlp.133
- Cite (ACL):
- Jiale Han, Bo Cheng, and Xu Wang. 2020. Open Domain Question Answering based on Text Enhanced Knowledge Graph with Hyperedge Infusion. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1475–1481, Online. Association for Computational Linguistics.
- Cite (Informal):
- Open Domain Question Answering based on Text Enhanced Knowledge Graph with Hyperedge Infusion (Han et al., Findings 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.findings-emnlp.133.pdf
- Data
- WebQuestionsSP