研究人员开发了知识图谱检索和补全的新方法

报道来源 [3]

1. arXiv cs.AI TIER_1 English(EN) · Joaqu\'in Polonuer (Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA, Departamento de Computaci\'on, FCEyN, Universidad de Buenos Aires, Buenos Aires, Argentina), Lucas Vittor (Department of Biomedical Informatics, Harvard Med · 2026-04-30 04:00

   自适应广度-深度检索的自主知识图谱探索

   arXiv:2601.13969v2 Announce Type: replace Abstract: Retrieving evidence for language model queries from knowledge graphs requires balancing broad search across the graph with multi-hop traversal to follow relational links. Similarity-based retrievers provide coverage but remain s…

2. arXiv cs.AI TIER_1 English(EN) · Bo Li · 2026-04-28 14:21

   RADD：用于多模态知识图谱补全的检索增强离散扩散模型

   Most multi-modal knowledge graph completion (MMKGC) models use one embedding scorer to do both retrieval over the full entity set and final decision making. We argue that this coupling is a core bottleneck: global high-recall search and local fine-grained disambiguation require d…

3. arXiv cs.CV TIER_1 English(EN) · Xu Yuan, Liangbo Ning, Qingqing Ye, Wenqi Fan, Qing Li · 2026-04-28 04:00

   mKG-RAG：在知识密集型VQA的检索增强生成中利用多模态知识图谱

   arXiv:2508.05318v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) has emerged as an effective paradigm for expanding the knowledge capacity of Multimodal Large Language Models (MLLMs) by incorporating external knowledge sources into the generation process, …