#graph-completion News & Analysis

2 articles tagged with #graph-completion. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles

AINeutralarXiv – CS AI · Feb 276/106

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Tokenization, Fusion and Decoupling: Bridging the Granularity Mismatch Between Large Language Models and Knowledge Graphs

Researchers propose KGT, a novel framework that bridges the gap between Large Language Models and Knowledge Graph Completion by using dedicated entity tokens for full-space prediction. The approach addresses fundamental granularity mismatches through specialized tokenization, feature fusion, and decoupled prediction mechanisms.

AINeutralarXiv – CS AI · Mar 175/10

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OMNIA: Closing the Loop by Leveraging LLMs for Knowledge Graph Completion

Researchers present OMNIA, a two-stage AI approach that combines structural and semantic reasoning to improve Knowledge Graph Completion using Large Language Models. The method clusters semantically related entities and validates them through embedding filtering and LLM-based validation, showing significant improvements in F1-scores compared to traditional models.