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eSPARQL: Representing and Reconciling Agnostic and Atheistic Beliefs in RDF-star Knowledge Graphs

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The Semantic Web – ISWC 2024 (ISWC 2024)

Abstract

Over the past few years, we have seen the emergence of large knowledge graphs combining information from multiple sources. Sometimes, this information is provided in the form of assertions about other assertions, defining contexts where assertions are valid. A recent extension to RDF which admits statements over statements, called RDF-star, is in revision to become a W3C standard. However, there is no proposal for a semantics of these RDF-star statements nor a built-in facility to operate over them. In this paper, we propose a query language for epistemic RDF-star metadata based on a four-valued logic, called eSPARQL. Our proposed query language extends SPARQL-star, the query language for RDF-star, with a new type of FROM clause to facilitate operating with multiple and sometimes conflicting beliefs. We show that the proposed query language can express four use case queries, including the following features: (i) querying the belief of an individual, (ii) the aggregating of beliefs, (iii) querying who is conflicting with somebody, and (iv) beliefs about beliefs (i.e., nesting of beliefs).

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Notes

  1. 1.

    In RDF, triples can also have other element types, namely blank nodes and literals. For simplicity, we do not consider them, since they do not change our results.

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Acknowledgements

This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under the DFG Germany’s Excellence Strategy – EXC 2120/1 – 390831618, and the DFG Excellence Strategy – EXC 2075 – 390740016. We acknowledge the support by the Stuttgart Center for Simulation Science (SimTech).

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Pan, X., Hernández, D., Seifer, P., Lämmel, R., Staab, S. (2025). eSPARQL: Representing and Reconciling Agnostic and Atheistic Beliefs in RDF-star Knowledge Graphs. In: Demartini, G., et al. The Semantic Web – ISWC 2024. ISWC 2024. Lecture Notes in Computer Science, vol 15232. Springer, Cham. https://doi.org/10.1007/978-3-031-77850-6_9

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  • DOI: https://doi.org/10.1007/978-3-031-77850-6_9

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