TECHS: Temporal Logical Graph Networks for Explainable Extrapolation Reasoning

Qika Lin, Jun Liu, Rui Mao, Fangzhi Xu, Erik Cambria

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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
Bibkey:
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)
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