Adaptive Path-Memory Network for Temporal Knowledge Graph Reasoning
Hao Dong, Zhiyuan Ning, Pengyang Wang, Ziyue Qiao, Pengfei Wang,, Yuanchun Zhou, Yanjie Fu

TL;DR
This paper introduces DaeMon, a novel relation-focused model for temporal knowledge graph reasoning that effectively captures temporal path information without relying on entity representations, leading to significant performance gains.
Contribution
The paper proposes a relation-based architecture with path memory for TKG reasoning, addressing entity scale issues and improving over state-of-the-art methods.
Findings
DaeMon outperforms existing models by up to 4.8% in MRR on real-world datasets.
The model effectively captures temporal path information without entity representations.
Extensive experiments validate the superiority of the proposed approach.
Abstract
Temporal knowledge graph (TKG) reasoning aims to predict the future missing facts based on historical information and has gained increasing research interest recently. Lots of works have been made to model the historical structural and temporal characteristics for the reasoning task. Most existing works model the graph structure mainly depending on entity representation. However, the magnitude of TKG entities in real-world scenarios is considerable, and an increasing number of new entities will arise as time goes on. Therefore, we propose a novel architecture modeling with relation feature of TKG, namely aDAptivE path-MemOry Network (DaeMon), which adaptively models the temporal path information between query subject and each object candidate across history time. It models the historical information without depending on entity representation. Specifically, DaeMon uses path memory to…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Graph Theory and Algorithms · Topic Modeling
