כתבה
arXiv cs.LG ·
SeedER: Seed-Expand-Retrieve לחיפוש יעיל בגרפים ידע
SeedER: Seed-Expand-Retrieve for Efficient Knowledge Graph Retrieval
חיפוש יעיל בגרפים ידע על ידי Graph Transformers ולמידת רפלקסיה. פיתוח חדשני שמציע פתרון זול ומעולה לבעיית החיפוש בגרפים ידע.
תקציר מקורי באנגליתarXiv:2605.23753v2 Announce Type: replace Abstract: Knowledge graphs (KGs) offer a rich representation for relational knowledge, but their irregular structure makes retrieval challenging: ego-graph expansion grows rapidly, and dense embedding methods struggle with multi-hop compositional queries. Several approaches use LLM agents to explore the KG, analyze candidate nodes, and decide where to explore next. While expressive, these approaches can incur substantial computational and memory costs. On the other hand, we show theoretically that dense embeddings precomputed for graph nodes, even with augmented structure and neighborhood-aware features, can require embedding dimensions comparable to the size of the graph to answer families of knowledge graph queries. This limitation can be overcom
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arxiv.org
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