Provenance-Enhanced Statements in Knowledge Graphs

• URL: http://arxiv.org/abs/2606.15246v1
• Date: Sat, 13 Jun 2026 11:01:50 GMT
• Title: Provenance-Enhanced Statements in Knowledge Graphs
• Abstract summary: Provenance-enhanced statements of the form "according to $X$, $varphi$" are pervasive in contemporary knowledge graphs.<n>Current provenance models can record who asserted what, but they typically treat provenance as semantically neutral.<n>We introduce DEC, a framework that interprets provenance predicates as indicators of epistemic stance and groups provenance-homogeneous sets of statements into cognitive worlds.
• Score: 0.08594140167290097
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Provenance-enhanced statements of the form "according to $X$, $\varphi$" are pervasive in contemporary knowledge graphs, especially in domains where graph content primarily represents claims, interpretations, and hypotheses (\emph{capta}) rather than observer-independent facts (\emph{data}). Current provenance models can record who asserted what, but they typically treat provenance as semantically neutral, leaving underspecified how attributed claims relate to factual commitment, to one another, and to reasoning. In this paper we introduce DEC, a framework that interprets provenance predicates as indicators of epistemic stance and groups provenance-homogeneous sets of statements into \emph{cognitive worlds}. Drawing on cognitive modal logics (doxastic, epistemic, and conjectural), DEC characterizes locality, rationality, and controlled permeation between cognitive worlds and a distinguished factual core ("reality"), thereby enabling principled reasoning over attributed content without collapsing disagreements into inconsistencies. We formalize a DEC interpretation for RDF datasets that is conservative over RDF~1.2 semantics, clarify the role of intensionality and identity (including the Superman paradox), and illustrate the approach on common Semantic Web representations (named graphs, quoted triples/RDF-star, and reification). Finally, we describe our prototype DEC reasoner implemented as a Fuseki dataset module, supporting controlled factualisation and explicit detection of disagreements and delusions.

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