Towards Data Science
GraphRAG: A Practitioner's Guide to 6 Advanced Architectural Patterns
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Towards Data Science
6d ago
GraphRAG with TypeSafe Jev: A System One Approach to Scalable Knowledge Graphs
The article discusses how calibrated decision models can manage high‑frequency graph decisions while large language models (LLMs) concentrate on reasoning, synthesis, and open‑ended generation. It introduces GraphRAG with TypeSafe Jev as a system‑one approach to building scalable knowledge graphs. The focus is on separating decision‑making from generative tasks to improve efficiency and reliability.
arXiv AI
Aug 28
From SQL to Knowledge Graphs: An LLM-Driven Multi-Agent Approach with Data Schema Improvement
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.
arXiv AI
Aug 18
Schema-Agnostic Graph Reasoning Agent for Hybrid Knowledge Graphs
arXiv:2608. 15834v1 Announce Type: new Abstract: Tool-calling LLM agents navigate unfamiliar codebases with a handful of generic primitives for listing, reading and searching files (ls, cat, grep).
arXiv AI
Jun 11
LLMs+Graphs: Toward Graph-Native, Synergistic AI Systems
arXiv:2606. 11560v1 Announce Type: cross Abstract: Large Language Models (LLMs) have advanced rapidly, but their limitations in structured and multi-hop reasoning underscore the need for graph-native, synergistic artificial intelligence (AI) systems.
Hugging Face Trending Papers
Jun 10
LLMs+Graphs: Toward Graph-Native, Synergistic AI Systems
Large Language Models (LLMs) have advanced rapidly, but their limitations in structured and multi-hop reasoning underscore the need for graph-native, synergistic artificial intelligence (AI) systems. Graph-structured data underpins critical applications across social, biological, financial, transportation, web, and knowledge domains, making it essential to understand how LLMs can leverage graph computation for grounded, context-rich inference.
arXiv AI
Jun 2
Knowledge Graphs as the Missing Data Layer for LLM-Based Industrial Asset Operations
arXiv:2605. 26874v2 Announce Type: replace-cross Abstract: LLM-based agents for industrial asset operations show limited accuracy when reasoning over flat document stores.