AGENTIC-SYSTEMSMay 21, 20261 min read7 views

Discoverable Agent Knowledge -- A Formal Framework for Agentic KG Affordances (Extended Version)

A new research paper introduces the Agentic Affordance Profile (AAP), a formal framework designed to help AI agents evaluate and select Knowledge Graphs effectively. By addressing the limitations of current metadata standards like VoID and DCAT, AAP enables principled KG selection, composition, and failure diagnosis during agent planning.

Computer Science > Artificial Intelligence

Title:Discoverable Agent Knowledge -- A Formal Framework for Agentic KG Affordances (Extended Version)

View PDF HTML (experimental)Abstract:Two decades ago, the Semantic Web Services community was asked how agents with different ontological commitments could discover, compose, and invoke web services coherently. The response was OWL-S and WSMO: formally grounded capability descriptions specifying what a service could do, what the agent must already know for invocation to be epistemically sound, and how ontological mismatches could be formally bridged. Current Knowledge Graph (KG) metadata standards such as VoID and DCAT describe what a KG contains yet say nothing about what a specific agent can prove from it, what closure assumptions govern empty results, or whether the agent's task vocabulary is grounded in the schema. Furthermore, in deployed KGs the governing schema DL and the operative entailment regime can diverge: an epistemic failure mode invisible to current metadata. We revisit and extend these insights for the KG setting with a four-dimensional formal framework from which we derive the Agentic Affordance Profile (AAP): a semantic layer above VoID and DCAT enabling principled KG selection, composition, and failure diagnosis at agent planning time. A five-point research agenda identifies the formal, computational, and engineering work needed to realise AAP-based affordance matching at scale.

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Source: arXiv cs.AI Recent

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