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arXiv:2602. 11745v2 Announce Type: replace Abstract: Graph models are fundamental to data analysis in domains rich with complex relationships.
The paper introduces a novel LLM‑driven multi‑agent pipeline that converts relational databases into graph databases by standardizing table and column names and iteratively refining the graph schema through ETL, Analyzer, and Graph agents. The resulting graph database meets accuracy, groundedness, and faithfulness criteria and shows significant performance gains, achieving 85.6% Q&A accuracy—12.12% higher than an SQL agent on PostgreSQL—and reducing latency by roughly threefold on a BFSI dataset. This demonstrates an efficient, automated method for transforming tabular data into a more intuitive and faster‑executing graph format.
post-graph-rag is an open‑source PostgreSQL‑native engine that unifies chunks, embeddings, a canonical entity graph, and community summaries in a single database, using pgvector for search and edge tables for traversal. It validates extraction output—rejecting vague predicates, normalising predicates, resolving entities to unique vertices, and flagging negations—before writing, and employs a bi‑temporal layer to record when a relation held and when the system believed it, superseding incompatible earlier assertions. In benchmarks against LightRAG, it builds denser, more queryable graphs and achieves higher scores on LongMemEval, largely due to its temporal grounding in prompts.
arXiv:2606. 29823v1 Announce Type: cross Abstract: The database community has repeatedly advanced the state of the art by recognizing that new workloads demand new system architectures.
arXiv:2606. 08481v1 Announce Type: cross Abstract: Enterprise property graphs vary widely in schema structure, internal terminology, domain assumptions, governance constraints, and user interaction patterns.
arXiv:2606. 11562v1 Announce Type: new Abstract: Graph analysis underlies many applications whose answers cannot be looked up in a single record or retrieved along a path: laundering rings, drug repurposing, user preference, and scientific theme are all inferred from a node together with its neighbourhood.