TECHS: Temporal Logical Graph Networks for Explainable Extrapolation Reasoning
Qika Lin, Jun Liu, Rui Mao, Fangzhi Xu, Erik Cambria · 2023
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.