The paper introduces CoSC, a method for knowledge graph completion that merges discrete structural coding with information from similar entities. An LLM first produces a candidate ranking based on structural codes, then refines this ranking using data from entities with comparable structures. Experiments on FB15k-237 demonstrate that CoSC achieves higher MRR and Hits@10 than existing baselines while staying competitive on Hits@1.
By Jiaqi Wang, Dongying Lin, Yang Yang, Yinan Liu, Bin Wang, Xiaochun Yang
Structure-guided NER optimization for enterprise GraphRAG systems The post Proxy-Pointer RAG: Eliminating Wasteful Entity & Relations Extraction in Knowledge Graphs appeared first on Towards Data Science .
By Partha Sarkar
arXiv:2606. 06003v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) fails systematically on queries requiring structural reasoning over interconnected entities.
By Grama Chethan
arXiv:2603.28773v2 Announce Type: replace-cross
Abstract: Large language models (LLMs) frequently generate confident yet factually incorrect content when used for language generation (a phenomenon of...
By Dobrik Georgiev, Kheeran K. Naidu, Alberto Cattaneo, Federico Monti, Carlo Luschi, Daniel Justus
arXiv:2607. 14095v1 Announce Type: new Abstract: Retrieval Augmented Generation (RAG) has proven to be a widely successful process at improving the quality of outputs from a Large Language Model (LLM) for wider context.
By Pranav Yadav
arXiv:2607. 07422v1 Announce Type: new Abstract: Logical Multi-Hop Query Answering over Knowledge Graphs (KGs) can be formulated as querying, with an implicit completeness assumption.
By Mayank Kharbanda, Michael Cochez, Rajiv Ratn Shah, Raghava Mutharaju