arXiv:2606.15246v1 Announce Type: cross 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,
This research addresses a growing need to better manage and interpret diverse, often conflicting, information within knowledge graphs as AI systems become more prevalent and complex.
Improving the handling of provenance and 'capta' versus 'data' in knowledge graphs is critical for the reliability, interpretability, and ethical deployment of AI applications, especially in sensitive domains.
Current knowledge graph models will evolve to incorporate more nuanced semantic provenance, moving beyond simple attributions to explicitly model factual commitment and the relationships between claims and reasoning.
- · AI developers
- · Data scientists
- · Knowledge graph providers
- · Sectors reliant on verifiable information
- · AI systems prioritizing quantity over quality of information
- · Applications with uncritical data integration
- · Users relying on unchallenged information in KGs
More robust and trustworthy AI applications due to clearer understanding of information sources and reliability.
Increased demand for tools and methodologies that implement advanced provenance tracking in data integration and AI pipelines.
Potentially, a shift in AI ethics frameworks to emphasize not just data privacy but also data veracity and semantic provenance explicit in models.
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Read at arXiv cs.AI