The paper introduces the "ontology tower," a narrow-and-deep ontology designed for a single industrial equipment system. By enriching knowledge through derived quantities and operational lessons, the ontology improves large language model agents’ performance on plant tasks, raising error avoidance by ~20 percentage points and matching the largest models’ scores. In live safety tests, agents guided the system’s controlled variable into target ranges in 12 of 14 runs.
arXiv:2610.19131v1 Announce Type: cross Abstract: Maintenance work orders are the largest written record of how industrial equipment fails, yet in most plants they are close to unusable for analysis....
arXiv:2604. 00555v5 Announce Type: replace Abstract: Enterprise adoption of Large Language Models (LLMs) is constrained by hallucination, domain drift, and the inability to enforce regulatory compliance at the reasoning level.
arXiv:2608.22974v1 Announce Type: new Abstract: Large language model (LLM) agents rely heavily on knowledge encoded in model parameters or presented as unstructured context. In domain-specific tasks,...
arXiv:2606. 28070v1 Announce Type: new Abstract: JD.
The paper presents a knowledge layer that reorganizes enterprise Retrieval-Augmented Generation (RAG) for multi‑agent systems by placing an ontology at the artifact level rather than on extracted entities. Using a seven‑agent reverse‑engineering pipeline, the authors processed 241 repositories across 18 services to generate thousands of artifacts that capture scope, journeys, rules, and acceptance criteria, each tagged with provenance metadata. The resulting self‑describing catalog, sparse metadata registry, and navigation hypergraph enable queries to resolve to artifact identifiers, thereby structurally disambiguating near‑identical rules and improving retrieval accuracy. The system was built in under a week and helped root‑cause over 100 production defects and attribute 35 release regressions to specific rules and services.
The paper introduces Knowledge Cards, a new structured artefact designed to capture validated knowledge about specific concepts that AI systems use to make decisions. Unlike existing model, data, and system cards, Knowledge Cards focus on the layer between inputs and outputs, documenting entities, relationships, reasoning patterns, conditions for validity, and provenance, all grounded in a formal domain ontology and signed off by a domain expert. Prototype cards have been created in the energy and pharmaceutical domains, and the schema is released as a public draft for community engagement.
The paper introduces an ontology-supported platform designed to facilitate the exchange, usage, and analysis of AI models and datasets. It addresses the need for effective management of AI assets in industrial settings by providing a structured framework that reduces semantic gaps. A real‑time critical systems use case demonstrates the platform’s practical utility.
The paper introduces Symbolic Separation, a method that grounds deep learning agents in knowledge graphs to improve reliability in operational data analytics. By restricting agent actions to an ontology-constrained Virtual Knowledge Graph with deterministic pre-execution validation, the approach transforms complex queries into validated graph traversals rather than relying on LLM-inferred joins. In experiments on 49.9 TB of supercomputer telemetry, the Neurosymbolic Deep Analyst achieved an 86% task‑success rate, eliminated silent data‑integrity errors, and reduced token costs by 2.4× compared to a non‑symbolic baseline.
arXiv:2610.36335v1 Announce Type: new Abstract: Large language models (LLMs) perform well across many real-world tasks but remain prone to hallucination, limiting their reliability in domains such as...
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.
arXiv:2605. 26874v2 Announce Type: replace-cross Abstract: LLM-based agents for industrial asset operations show limited accuracy when reasoning over flat document stores.
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.