AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
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 tuning.
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...
A Structure-Internalized Rule Language Model (SIRLM) is proposed, which centers on structural rule generation to couple the parametric learning of structural knowledge with the faithfulness evaluation of reasoning logic, enabling LLMs to anchor tightly to KG-grounded evidence.
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.
FITTER consistently outperforms inductive baselines without retraining, indicating that vocabulary-agnostic structural learning is a viable foundation for inference over the heterogeneous knowledge graphs of the Semantic Web.
This paper proposes DocTrace, a hierarchical framework that progressively performs evidence localization, structured document parsing, and evidence graph reasoning to enable explicit evidence provenance, and develops a two-stage training framework.
ViSR-KGC, a visual subgraph reasoning approach for KGC, integrates three complementary capabilities to capture semantic correlations: identifying global topology dependencies via representation learning, analyzing local multimodal evidence using VLMs, and providing necessary commonsense knowledge inherent in pre-traine...
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