Build2SPARQL is a large-scale benchmark dataset for translating natural-language questions into SPARQL queries over building knowledge graphs. The dataset is generated by a KG‑grounded pipeline that produces 6,136 executable SPARQL queries and 30,680 corresponding natural-language questions across six query-pattern families and five vocabulary registers, covering 201 building KGs. Human validation shows high semantic fidelity, naturalness, and operational plausibility, and retrieval‑augmented evaluation demonstrates significant accuracy gains for open‑weight language models.
By Wooyoung Jung
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.
By Harshdeep Singh, Yurui Zhu, Giovanni Colavizza, Matteo Romanello
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.
By Victor Eiti Yamamoto, Takeda Hideaki, Yamamoto Yasunori
arXiv:2508. 01815v2 Announce Type: replace-cross Abstract: Text-to-SPARQL maps natural-language questions to executable SPARQL queries over RDF knowledge graphs.
By Yang Zhao, Chengxiao Dai, Yue Xiu, Dusit Niyato
arXiv:2607. 18029v1 Announce Type: cross Abstract: Researchers need to answer ad-hoc questions about the contents of domain-specific archives but often lack the expertise to write structured queries on the metadata.
By Blake G. Fitch, Cato Elia Kurtz
arXiv:2602. 11745v2 Announce Type: replace Abstract: Graph models are fundamental to data analysis in domains rich with complex relationships.
By Songlin Lyu, Lujie Ban, Zihang Wu, Tianqi Luo, Jirong Liu, Ayoub Moussaid, Oskar van Rest, Heng Lin, Chenhao Ma, Nan Tang, Shipeng Qi, Yongchao Liu, Zhan Qiu, Juelu Zhang, Jiajun Zheng
arXiv:2609.14652v1 Announce Type: cross
Abstract: Large Language Model (LLM) applications often transfer domain concepts into the model's context informally, through prompt prose, schema dumps, and e...
By Blake G. Fitch
arXiv:2608.29345v1 Announce Type: new
Abstract: While recent Large Language Model (LLM)-based text-to-SQL systems achieve impressive performance on standard benchmarks, they struggle when user querie...
By Yunfan Zhou, Qiming Shi, Yizhou Yang, Di Weng, Yingcai Wu
text2ql is an open‑source Python framework that enables natural language querying of databases without being limited to SQL, without requiring large language model inference at query time, and with a runtime confidence score for each generated query. It uses a language‑agnostic Intermediate Representation (QueryIR) and a pluggable renderer that supports both SQL and GraphQL through a single seven‑stage detection pipeline. In deterministic mode, it achieves 100% execution accuracy with a median latency of 3.2 ms, while the LLM‑backed mode reaches 62‑70% exact match and 84‑91% execution accuracy on benchmark samples.
Retrieval in the SQL setting has largely been studied as the task of finding, within a large collection of SQL statements, the statement that answers a natural-language question. At scale, however, a more fundamental retrieval problem precedes generation: schema retrieval, identifying the tables and columns a question requires in a database that may contain thousands of them, far more than fit in a model's context.
text2ql is an open‑source Python framework that enables natural language querying of databases without relying on large language models at query time. It uses a language‑agnostic intermediate representation (QueryIR) and a pluggable renderer to support both SQL and GraphQL targets through a single seven‑stage detection pipeline. In deterministic mode, it achieves 100% execution accuracy with a median latency of 3.2 ms, while the LLM‑backed mode delivers 62‑70% exact match and 84‑91% execution accuracy on benchmark samples.
By Ritesh Kumar
arXiv:2608. 07700v1 Announce Type: new Abstract: Translating a natural-language question into a SPARQL query that can be executed against a large knowledge graph requires resolving lexical ambiguity, grounding surface terms in the target ontology, and producing graph patterns that are both syntactically valid and semantically faithful.
By Tommaso Soru, Abdulsobur Oyewale