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New PROV-CRED extension enhances knowledge graph fact reliability for AI

By PulseAugur Editorial · [1 sources] ·

A new extension to the PROV-O ontology, called PROV-CRED, has been developed to address the issue of varying accuracy in knowledge graph facts. This extension allows for the reliability, AI model performance, and accreditation of data triples to be queried. The goal is to prevent compounding errors in AI reasoning by making data provenance more transparent and evaluable. This work was presented by Mary Ann Tan at the SEMANTICS 2026 conference. AI

IMPACT Enhances AI reasoning by improving the reliability and queryability of knowledge graph data.

RANK_REASON The item describes a new extension to an ontology for knowledge graphs, presented at a conference, which is a research-oriented development. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Mastodon — mastodon.social →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New PROV-CRED extension enhances knowledge graph fact reliability for AI

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The item describes a new extension to an ontology for knowledge graphs, presented at a conference, which is a research-oriented development. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Mastodon — mastodon.social TIER_1 English(EN) · [email protected] ·

    Stop treating all knowledge graph facts as equally accurate—PROV-O extension PROV-CRED makes a triple’s reliability, AI model performance, and accreditation ful

    Stop treating all knowledge graph facts as equally accurate—PROV-O extension PROV-CRED makes a triple’s reliability, AI model performance, and accreditation fully queryable to prevent hallucinated errors from compounding in AI reasoning. Mary Ann Tan presenting her poster "PROV-C…

    LINKS zenodo.org/…/22945873

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