The paper proposes a four‑dimensional formal framework—Semantic Expressivity, Agentic Discoverability, Task‑Relative Grounding, and Epistemic Trust Scope—to extend current KG metadata standards (VoID and DCAT). It introduces the Agentic Affordance Profile (AAP), a semantic layer that enables agents to select, compose, and diagnose failures in knowledge graphs at planning time. A scholarly‑search example illustrates the framework and outlines a five‑point research agenda for scaling AAP‑based affordance matching.
By Terry R. Payne, Valentina Tamma, Enrico Daga
arXiv:2607. 02609v1 Announce Type: cross Abstract: For decades, data engineering has developed mature architectural principles for integrating, governing, validating, cataloging, and serving organizational data.
By Mariano Garralda-Barrio
arXiv:2603. 14805v2 Announce Type: replace Abstract: Enterprise software organizations accumulate critical institutional knowledge - architectural decisions, deployment procedures, compliance policies, incident playbooks - yet this knowledge remains trapped in formats designed for human interpretation.
By Gal Bakal
arXiv:2605. 22093v3 Announce Type: replace Abstract: Knowledge graphs have become the primary vehicle for data integration and are critical to the success of modern AI, but the diversity of KG modelling practices, from lightweight vocabularies to richly axiomatised ontologies, makes integration and reuse expensive and brittle.
By Enrico Daga, Valentina Tamma, Terry Payne
arXiv:2608. 15193v1 Announce Type: cross Abstract: As large language model (LLM) agents are increasingly adopted in scientific research, external knowledge bases, knowledge graphs, and long-term memory have improved information retrieval and task continuity.
By Yuyang Zheng, Nan Li, Wenxia Deng, Lige Yan, Xiang Li, Si Chen
arXiv:2604. 21744v2 Announce Type: replace-cross Abstract: The capabilities of AI-assisted coding are progressing at breakneck speed.
By Magnus Palmblad, Jared M. Ragland, Benjamin A. Neely