Historically Relevant Event Structuring for Temporal Knowledge Graph Reasoning
Jinchuan Zhang, Ming Sun, Chong Mu, Jinhao Zhang, Quanjiang Guo, Ling Tian

TL;DR
This paper introduces HisRES, a novel approach for temporal knowledge graph reasoning that effectively captures multi-granular recent and historically relevant events, leading to improved predictive performance.
Contribution
HisRES is the first method to integrate multi-granularity evolution encoding with global relevance modeling for TKG reasoning.
Findings
Achieves state-of-the-art results on four benchmarks.
Effectively models structural and temporal dependencies.
Demonstrates the importance of historical relevance in reasoning.
Abstract
Temporal Knowledge Graph (TKG) reasoning focuses on predicting events through historical information within snapshots distributed on a timeline. Existing studies mainly concentrate on two perspectives of leveraging the history of TKGs, including capturing evolution of each recent snapshot or correlations among global historical facts. Despite the achieved significant accomplishments, these models still fall short of I) investigating the impact of multi-granular interactions across recent snapshots, and II) harnessing the expressive semantics of significant links accorded with queries throughout the entire history, particularly events exerting a profound impact on the future. These inadequacies restrict representation ability to reflect historical dependencies and future trends thoroughly. To overcome these drawbacks, we propose an innovative TKG reasoning approach towards…
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Taxonomy
TopicsSemantic Web and Ontologies · Service-Oriented Architecture and Web Services · Cognitive Computing and Networks
