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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.</abstract>
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%0 Conference Proceedings
%T TECHS: Temporal Logical Graph Networks for Explainable Extrapolation Reasoning
%A Lin, Qika
%A Liu, Jun
%A Mao, Rui
%A Xu, Fangzhi
%A Cambria, Erik
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F lin-etal-2023-techs
%X 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.
%R 10.18653/v1/2023.acl-long.71
%U https://aclanthology.org/2023.acl-long.71
%U https://doi.org/10.18653/v1/2023.acl-long.71
%P 1281-1293

Markdown (Informal)

[TECHS: Temporal Logical Graph Networks for Explainable Extrapolation Reasoning](https://aclanthology.org/2023.acl-long.71) (Lin et al., ACL 2023)

• TECHS: Temporal Logical Graph Networks for Explainable Extrapolation Reasoning (Lin et al., ACL 2023)

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.