concept

SPARQL

Facts (17)

Sources

Construction of Knowledge Graphs: State and Challenges - arXiv arxiv.org arXiv 4 facts

claimSPARQL is the primary standardized query language for RDF and the Semantic Web, with an extended version called SPARQL-Star used for RDF-Star.

claimSPARQL-Generate is an extension of SPARQL that supports the transformation of streaming and binary data sources.

referenceA. Azzam et al. introduced 'SMART-KG', a hybrid shipping method for SPARQL querying on the Web, at The Web Conference 2020 (WWW '20).

referenceBakerally developed a SPARQL extension designed to generate RDF data from heterogeneous formats, as presented at the Extended Semantic Web Conference in 2017.

Integrating Knowledge Graphs into RAG-Based LLMs to Improve ... thesis.unipd.it Università degli Studi di Padova 3 facts

procedureThe proposed method for integrating knowledge graphs with LLMs utilizes Named Entity Recognition (NER) and Named Entity Linking (NEL) combined with SPARQL queries directed at the DBpedia knowledge graph.

procedureThe proposed method in the thesis integrates knowledge graphs with Large Language Models by combining Named Entity Recognition (NER) and Named Entity Linking (NEL) with SPARQL queries to the DBpedia knowledge graph.

procedureThe proposed fact-checking method utilizes a system that combines Named Entity Recognition (NER) and Named Entity Linking (NEL) with SPARQL queries directed at the DBpedia knowledge graph.

Context Graph vs Knowledge Graph: Key Differences for AI - Atlan atlan.com Atlan Jan 27, 2026 2 facts

claimKnowledge graphs utilize SPARQL or Cypher for semantic traversal and inference, while context graphs utilize graph traversal with operational and policy-aware filters.

referenceKnowledge graphs are queried using SPARQL for triple stores or Cypher for property graphs, whereas context graphs utilize graph queries combined with operational filters to find assets based on quality, certification, and modification history.

KG-RAG: Bridging the Gap Between Knowledge and Creativity - arXiv arxiv.org arXiv May 20, 2024 2 facts

measurementThe ComplexWebQuestions dataset contains 34,689 complex questions, each associated with an average of 367 Google web snippets and corresponding SPARQL queries for the Freebase knowledge graph.

Grounding LLM Reasoning with Knowledge Graphs - arXiv arxiv.org arXiv Dec 4, 2025 1 fact

procedureThere are four primary methods for integrating Knowledge Graphs with Large Language Models: (1) learning graph representations, (2) using Graph Neural Network (GNN) retrievers to extract entities as text input, (3) generating code like SPARQL queries to retrieve information, and (4) using step-by-step interaction methods for iterative reasoning.

LLM-KG4QA: Large Language Models and Knowledge Graphs for ... github.com GitHub 1 fact

referenceThe field of Natural Language to Graph Query Language (NL2GQL) research focuses on translating natural language questions into graph query languages like Cypher or SPARQL, often utilizing Large Language Models to bridge the gap between natural language and structured graph databases.

LLM Knowledge Graph: Merging AI with Structured Data - PuppyGraph puppygraph.com PuppyGraph Feb 19, 2026 1 fact

claimGraphRAG systems abstract traditional database interactions, allowing users to query systems using natural language instead of specialized query languages like Cypher, Gremlin, or SPARQL.

Knowledge Graphs: Opportunities and Challenges - Springer Nature link.springer.com Springer Apr 3, 2023 1 fact

claimKnowledge graph-based question-answering systems facilitate the answering task by either using similarity measures or by producing structured queries in standard formats such as SPARQL.

Large Language Models Meet Knowledge Graphs for Question ... arxiv.org arXiv Sep 22, 2025 1 fact

referenceWebQSP (tau Yih et al., 2016) is a Knowledge-Based Question Answering (KBQA) dataset that includes SPARQL queries for knowledge-based question answering.

RAG Hallucinations: Retrieval Success ≠ Generation Accuracy linkedin.com Sumit Umbardand · LinkedIn Feb 6, 2026 1 fact

procedureA tutorial pipeline for processing PDF documents generates clean text and sentence-level inputs for NLP, RDF/Turtle files capturing entities and relationship triples, a Fuseki dataset queryable via SPARQL, and an optional ontology draft for refinement in Protégé.