AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This work proposes a multi-granularity knowledge refinement approach to prune historical TKGs, which selectively removes irrelevant edges and unnecessary nodes at both the edge and node levels.
Temporal knowledge graph (TKG) reasoning is critical for modeling and forecasting the evolution of real-world events. Existing TKG construction pipelines transform raw text into structured temporal quadruples as graph facts. However, in this process, they often fail to preserve reasoning-relevant contextual semantics f...
AG is proposed, a Structure-Aware Generative framework for temporal knowledge graph reasoning with historical evidence that first constructs dual-view historical evidence to better capture structural dependencies in temporal knowledge graphs and formulates TKGR as an end-to-end generative task through instruction tunin...
The Temporal-Weighted Transfer Network (TWTNET), a novel reasoning model that jointly leverages statistical modeling and historical information transfer, is proposed, which achieves more robust and interpretable extrapolative reasoning.
A Multi-Granularity Implicit Temporal (MGIT) framework that enhances temporal representation and reasoning by modeling implicit temporal dependencies across different granularities is proposed, highlighting its effectiveness in capturing implicit temporal information and enhancing multi-granularity temporal reasoning.
Knowledge graph completion (KGC) aims to infer missing facts based on existing triplets. Traditional path-based methods formulate reasoning as a discrete multi-hop traversal, which suffers from exponential search space and error accumulation.In this paper, we propose FlowPath, which rethinks the reasoning process by tr...
A multi-modal data-driven knowledge graph reasoning model integrating unified encoding, graph representation learning, and rule constraints is proposed, indicating better relation completion accuracy, reasoning stability, and inference efficiency in multi-modal knowledge graph reasoning tasks.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.