GraphRAGProv is an architecture that combines GraphRAG with how‑provenance by executing SPARQL queries over source‑grounded RDF graphs to compute answers and their provenance polynomials. The polynomials record which statement occurrences were used and how they were combined, linking each occurrence to its source text so users can verify the extraction and query process. An interactive demo evaluated 400 cases from two multi‑hop QA benchmarks, uncovering extraction and query errors even when the final answer was correct.
arXiv:2508. 01815v2 Announce Type: replace-cross Abstract: Text-to-SPARQL maps natural-language questions to executable SPARQL queries over RDF knowledge graphs.
VeriCypher is an LLM-based framework that generates Cypher queries for question answering over Labeled Property Graphs (LPGs). It uses schema summaries to condition query generation, executes queries, evaluates results with an LLM, and iteratively corrects errors through named-entity verification. Experiments on CypherBench show that VeriCypher consistently improves answer correctness and query robustness compared to a baseline, and ablation studies confirm that named-entity verification corrects hallucinated schema elements and entity values.
The paper introduces EXYGEN, a framework that enables conversational access to large knowledge graphs by combining VoID descriptions, ShEx schemas, retrieved triples, and example question‑query pairs in a retrieval‑augmented generation pipeline. On the SciQA benchmark, this approach achieves an exact‑match score of 0.419 without fine‑tuning any large language model, and shows that larger general‑purpose LLMs can outperform smaller code‑specialized ones when provided sufficient context. To scale metadata generation for very large KGs, the authors propose a predicate‑coverage‑aware parallel graph sampling strategy that preserves structural diversity, reduces runtime by over 80× on OpenCitations Meta and GESIS, and is the only tractable method for obtaining complete metadata on ORKG.
SPARQL-LLM is an open‑source, triplestore‑agnostic system that generates SPARQL queries from natural language using lightweight metadata and dedicated components for indexing, prompt building, and execution. It achieves up to 59 % higher F1 scores than the next best system on a multilingual challenge and on bioinformatics knowledge graphs, while being up to 27 × faster and costing no more than $0.01 per question. The project is publicly available on GitHub and is already deployed on real‑world decentralized knowledge graphs such as expasy.org/chat.
The paper introduces Federated Graph Retrieval-Augmented Generation (FedGRAG), a framework that extends Graph Retrieval-Augmented Generation to federated settings where knowledge graphs are distributed across multiple clients. FedGRAG tackles the lack of a global graph by using a Federated Knowledge Routing mechanism that identifies relevant clients via lightweight indices, and it protects sensitive local graph data through a controlled-disclosure evidence construction method that abstracts retrieved subgraphs into evidence abstracts for server-side reconstruction. Experiments show that FedGRAG outperforms existing baselines in both retrieval and downstream generation tasks, establishing a new paradigm for deploying GraphRAG in federated environments.
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.
Enrich-on-Graph (EoG) is a flexible framework that uses large language models to enrich knowledge graphs, thereby bridging the semantic gap between structured graphs and unstructured queries in complex reasoning tasks. By leveraging LLMs’ prior knowledge, EoG enables efficient evidence extraction from knowledge graphs, achieving precise and robust reasoning while maintaining low computational costs and scalability. The authors also introduce three graph quality evaluation metrics for query‑graph alignment, theoretically validate their optimization objectives, and demonstrate state‑of‑the‑art performance on two KGQA benchmark datasets.
arXiv:2511.04473v3 Announce Type: replace Abstract: Retrieval of information from graph-structured knowledge bases represents a promising direction for improving the factuality of LLMs. While various...
arXiv:2606. 05415v1 Announce Type: cross Abstract: Real-world data spans tables, documents, and semi-structured files with implicit semantics.
arXiv:2606. 05901v1 Announce Type: cross Abstract: Large language models (LLMs) have fundamentally transformed the landscape of Natural Language Processing.
RENSA is a federated SPARQL query generation framework that extends SPARQL Builder Metadata to include class and authority information, enabling precise source selection and semantic constraint inference without runtime ASK queries. The generated metadata profiles occupy less than 1% of the original dataset triples, providing storage‑efficient insights. Evaluation on the LargeRDFBench benchmark shows that RENSA matches state‑of‑the‑art source selection performance while eliminating runtime communication overhead.
arXiv:2603. 29875v3 Announce Type: replace-cross Abstract: One of the key problems in Retrieval-augmented generation (RAG) systems is that chunk-based retrieval pipelines represent the source chunks as atomic objects, mixing the information contained within such a chunk into a single vector.