TECHS: Temporal Logical Graph Networks for Explainable Extrapolation Reasoning
Abstract
Extrapolation reasoning on temporal knowledge graphs (TKGs) aims to forecast future facts based on past counterparts. There are two main challenges: (1) incorporating the complex information, including structural dependencies, temporal dynamics, and hidden logical rules; (2) implementing differentiable logical rule learning and reasoning for explainability. To this end, we propose an explainable extrapolation reasoning framework TEemporal logiCal grapH networkS (TECHS), which mainly contains a temporal graph encoder and a logical decoder. The former employs a graph convolutional network with temporal encoding and heterogeneous attention to embed topological structures and temporal dynamics. The latter integrates propositional reasoning and first-order reasoning by introducing a reasoning graph that iteratively expands to find the answer. A forward message-passing mechanism is also proposed to update node representations, and their propositional and first-order attention scores. Experimental results demonstrate that it outperforms state-of-the-art baselines.- Anthology ID:
- 2023.acl-long.71
- Volume:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1281–1293
- Language:
- URL:
- https://aclanthology.org/2023.acl-long.71
- DOI:
- 10.18653/v1/2023.acl-long.71
- Cite (ACL):
- Qika Lin, Jun Liu, Rui Mao, Fangzhi Xu, and Erik Cambria. 2023. TECHS: Temporal Logical Graph Networks for Explainable Extrapolation Reasoning. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1281–1293, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- TECHS: Temporal Logical Graph Networks for Explainable Extrapolation Reasoning (Lin et al., ACL 2023)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/2023.acl-long.71.pdf