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A Framework for Federated SPARQL Query Processing over Heterogeneous Linked Data Fragments

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arxiv 2102.03269 v2 pith:XCQZAGNK submitted 2021-02-05 cs.DB

classification cs.DB
keywords interfacesqueryfederationssparqlframeworkheterogeneousprocessingdata
verification ladder T0 review T1 audit T2 compute T3 formal
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Linked Data Fragments (LDFs) refer to Web interfaces that allow for accessing and querying Knowledge Graphs on the Web. These interfaces, such as SPARQL endpoints or Triple Pattern Fragment servers, differ in the SPARQL expressions they can evaluate and the metadata they provide. Client-side query processing approaches have been proposed and are tailored to evaluate queries over individual interfaces. Moreover, federated query processing has focused on federations with a single type of LDF interface, typically SPARQL endpoints. In this work, we address the challenges of SPARQL query processing over federations with heterogeneous LDF interfaces. To this end, we formalize the concept of federations of Linked Data Fragment and propose a framework for federated querying over heterogeneous federations with different LDF interfaces. The framework comprises query decomposition, query planning, and physical operators adapted to the particularities of different LDF interfaces. Further, we propose an approach for each component of our framework and evaluate them in an experimental study on the well-known FedBench benchmark. The results show a substantial improvement in performance achieved by devising these interface-aware approaches exploiting the capabilities of heterogeneous interfaces in federations.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 2 citations worldwide. Full citation record

  1. MetaboT: An LLM-based Multi-Agent Frameworkfor Interactive Analysis of Mass SpectrometryMetabolomics Knowledge Graphs

    cs.AI 2025-10 conditional novelty 6.0 of 10

    A multi-agent LLM system converts natural-language metabolomics questions into SPARQL queries over the ENPKG knowledge graph, reaching 83.67% accuracy with GPT-4o versus 8.16% for the standalone model.

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