arXiv:2605.23753v1 Announce Type: new 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. Existing agent-based graph exploration approaches, while expressive, are often too expensive for large-scale retrieval. We introduce SeedER (Seed-and-Expand Retrieval), a retrieval framework that explicitly leverages KG structure through iterative, low-cost expansion. SeedER first seeds a compact set of c
The increasing complexity and scale of knowledge graphs in AI demand more efficient and scalable retrieval methods, driving innovations like SeedER.
Improved knowledge graph retrieval can significantly enhance the capabilities of AI agents and large language models across various applications, from research to enterprise solutions.
The ability to perform more efficient and less computationally expensive multi-hop queries on large knowledge graphs could accelerate the development of more sophisticated AI applications.
- · AI agents developers
- · Knowledge graph vendors
- · Research institutions
- · Companies with complex data environments
- · Inefficient knowledge graph exploration methods
More sophisticated AI applications powered by enhanced knowledge retrieval.
Accelerated development and adoption of AI systems that rely on deep contextual understanding.
Potential for new business models and services built around advanced knowledge reasoning capabilities.
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Read at arXiv cs.LG