HMGI统一向量与图搜索，实现复杂关系下的高效语义查询。
The Hybrid Multimodal Graph Index (HMGI): A Comprehensive Framework for Integrated Relational and Vector Search
- 将向量搜索与图遍历结合，用图数据库架构支持混合查询。
- 在关系密集场景下比Milvus快，混合查询时间接近亚线性。
- 支持动态数据更新，按模态分区优化索引性能。
复杂多模态数据的兴起暴露了专用向量数据库与传统图数据库之间的能力鸿沟：向量数据库擅长语义相似性搜索，但缺乏深层关系查询能力；图数据库擅长复杂遍历，却不原生支持高维向量搜索。本文提出混合多模态图索引（HMGI），一种统一框架，旨在通过集成近似最近邻搜索（ANNS）与表达性强的图遍历查询，实现对多模态数据的高效混合查询。核心创新包括：基于模态感知的嵌入分区策略，以优化索引结构和查询性能；以及借鉴TigerVector架构的自适应、低开销索引更新机制，支持动态数据摄入。通过将语义相似性搜索与关系上下文直接融合，HMGI旨在超越纯向量数据库（如Milvus）在关系密集型查询中的表现，并在混合任务中实现亚线性查询时间。
原文摘要 · Abstract (English)
The proliferation of complex, multimodal datasets has exposed a critical gap between the capabilities of specialized vector databases and traditional graph databases. While vector databases excel at semantic similarity search, they lack the capacity for deep relational querying. Conversely, graph databases master complex traversals but are not natively optimized for high-dimensional vector search. This paper introduces the Hybrid Multimodal Graph Index (HMGI), a novel framework designed to bridge this gap by creating a unified system for efficient, hybrid queries on multimodal data. HMGI leverages the native graph database architecture and integrated vector search capabilities, exemplified by platforms like Neo4j, to combine Approximate Nearest Neighbor Search (ANNS) with expressive graph traversal queries. Key innovations of the HMGI framework include modality-aware partitioning of embeddings to optimize index structure and query performance, and a system for adaptive, low-overhead index updates to support dynamic data ingestion, drawing inspiration from the architectural principles of systems like TigerVector. By integrating semantic similarity search directly with relational context, HMGI aims to outperform pure vector databases like Milvus in complex, relationship-heavy query scenarios and achieve sub-linear query times for hybrid tasks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。