Multi-Granularity Context-Enhanced RAG over Multimodal Knowledge Graphs
Retrieval-augmented generation (RAG) is widely used to mitigate hallucination issues in large language models (LLMs) and multimodal large language models (MLLMs). In particular, knowledge graph (KG)-based RAG leverages structured knowledge to provide (M)LLMs with high-quality external information. Building on these works, recent studies have explored multimodal knowledge graphs (MMKGs) as knowledge bases for GraphRAG. This enables Graph RAG to integrate knowledge across multiple modalities, thereby further enhancing its performance. However, existing MMKG-based RAG methods generally follow a c
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- PossiblePossibly related (embedding) · 28%microsoft/graphrag →
“Possibly related via embedding similarity 0.62 (not asserted). Timestamp check: artifact slightly before paper (-41d).”
- PossiblePossibly related (embedding) · 57%EY re-envisions RAG around multimodal knowledge graphs to improve accuracy - SiliconANGLE →
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%hiyouga/LlamaFactory →
“Shared author/contributor keys: lin”
- FuzzyOverlapping authors or contributors · 62%Zeyi-Lin/HivisionIDPhotos →
“Shared author/contributor keys: lin”
- LinkedLinked via arxiv author · 85%Zongyu Wu →
“Multi-Granularity Context-Enhanced RAG over Multimodal Knowledge Graphs”
- LinkedLinked via arxiv author · 85%Yilong Wang →
“Multi-Granularity Context-Enhanced RAG over Multimodal Knowledge Graphs”