TY - UNPB
T1 - Agentic SPARQL
T2 - Evaluating SPARQL-MCP-powered Intelligent Agents on the Federated KGQA Benchmark
AU - Dobriy, Daniel
AU - Bauer, Frederik
AU - Azzam, Amr
AU - Banerjee, Debayan
AU - Polleres, Axel
PY - 2026/1
Y1 - 2026/1
N2 - Standard protocols such as the Model Context Protocol (MCP) that allow LLMs to connect to tools have recently boosted "agentic" AI applications, which, powered by LLMs' planning capabilities, promise to solve complex tasks with the access of external tools and data sources. In this context, publicly available SPARQL endpoints offer a natural connection to combine various data sources through MCP by (a) implementing a standardised protocol and query language, (b) standardised metadata formats, and (c) the native capability to federate queries. In the present paper, we explore the potential of SPARQL-MCP-based intelligent agents to facilitate federated SPARQL querying: firstly, we discuss how to extend an existing Knowledge Graph Question Answering benchmark towards agentic federated Knowledge Graph Question Answering (FKGQA); secondly, we implement and evaluate the ability of integrating SPARQL federation with LLM agents via MCP (incl. endpoint discovery/source selection, schema exploration, and query formulation), comparing different architectural options against the extended benchmark. Our work complements and extends prior work on automated SPARQL query federation towards fruitful combinations with agentic AI.
AB - Standard protocols such as the Model Context Protocol (MCP) that allow LLMs to connect to tools have recently boosted "agentic" AI applications, which, powered by LLMs' planning capabilities, promise to solve complex tasks with the access of external tools and data sources. In this context, publicly available SPARQL endpoints offer a natural connection to combine various data sources through MCP by (a) implementing a standardised protocol and query language, (b) standardised metadata formats, and (c) the native capability to federate queries. In the present paper, we explore the potential of SPARQL-MCP-based intelligent agents to facilitate federated SPARQL querying: firstly, we discuss how to extend an existing Knowledge Graph Question Answering benchmark towards agentic federated Knowledge Graph Question Answering (FKGQA); secondly, we implement and evaluate the ability of integrating SPARQL federation with LLM agents via MCP (incl. endpoint discovery/source selection, schema exploration, and query formulation), comparing different architectural options against the extended benchmark. Our work complements and extends prior work on automated SPARQL query federation towards fruitful combinations with agentic AI.
KW - Agentic SPARQL
KW - Knowledge Graphs
KW - AI Agents
KW - SPARQL
KW - Artifical Intelligence
U2 - 10.57938/83c86964-2d48-46f1-b655-5bef78c1a837
DO - 10.57938/83c86964-2d48-46f1-b655-5bef78c1a837
M3 - Working Paper/Preprint
T3 - Working Papers on Information Systems, Information Business and Operations
BT - Agentic SPARQL
ER -