ontology

This webinar provides a practical engineering-focused approach to building a semantic layer on Neo4j using ontology, emphasizing benefits for AI-driven applications and data consistency.

The paper introduces the Knowledge Card, a structured and expert-validated artifact designed to represent knowledge in AI systems, particularly for agentic AI, enhancing transparency and auditability.

This paper proposes a SAREF-compliant ontology for representing and orchestrating distributed AI workflows across heterogeneous edge, fog, and cloud environments to improve semantic interoperability and resource-aware deployment.

The paper introduces an ontology-driven framework to quantify and enforce structural consistency in document-level relation extraction datasets, reducing logical contradictions and improving model generalization when using distant supervision data.

Presents Moose, a neuro-symbolic method that compiles OWL 2 EL ontologies into Sentential Decision Diagrams for differentiable weighted model counting, enabling latent concept learning under partial supervision and providing the first reasoning-shortcut analysis in an OWL EL setting.

This paper presents a large collection of 57.5K transactional prompts extracted from GitHub, introducing an ontology to analyze their linguistic structure and usage patterns across languages, tasks, and modalities.

This paper proposes a hybrid knowledge graph generation pipeline that combines top-down grounding in Wikidata with bottom-up agentic synthesis to handle noisy, multilingual HR skill declarations, producing a scalable and self-healing skills taxonomy.

This paper proposes a neuro-symbolic closed-loop architecture for laser powder bed fusion, where an in-loop ontology couples symbolic reasoning with statistical learning to control melt pool depth and eliminate overhang dross. Feasibility is demonstrated via a surrogate calibrated to the NIST AM-Bench benchmark.

This paper proposes the Mecellem semantic protocol, an ontologically grounded framework for artificial legal intelligence, arguing that legal reasoning requires dynamic, context-dependent meaning construction rather than mere codification or statistical pattern recognition.

This paper presents FDD-ON, a modular ontology for representing VAV HVAC system components, faults, symptoms, and impacts to enable interoperable fault detection and diagnostics applications.

Presents SciToolAgent-Evo, an ontology-aware self-evolving LLM agent for open-world scientific tool acquisition, along with the OpenSciToolBench benchmark of 900 realistic tasks. The agent uses an evolving memory and LinUCB-based bandit gate to dynamically explore and acquire novel tools.

GreptimeDB introduces a semantic layer that preserves OTLP metadata (instrument kind, unit, temporality) which is normally discarded at ingestion, enabling AIOps tools and LLM agents to understand system topology without guessing from column names.

OntoBook converts medical ontology graphs into synthetic textbook prose using LLMs, then uses the resulting 1.3M French textbooks to pretrain ModernCamemBERT, achieving significant gains on medical coding benchmarks.

This paper presents a combined proof-of-mechanism study of ontology-amplified distillation for sovereign enterprise language models and a contextuality-audit method, using a Qwen3.6-27B student adapted via supervised fine-tuning and DPO. The results are underpowered and negative, showing no superiority over frontier baselines and zero contextuality in routing.

A preprint arguing that AI can map known biology but cannot perform 'kind formation'—the minting of new variables and constraints—which requires the embodied engagement of human scientists. The authors propose a curriculum to train scientists as 'detectors of the unparameterized' to complement AI in biological discovery.

Databricks introduces Genie Ontology, a self-improving context layer on Unity Catalog that builds a living knowledge graph of business definitions, using OntoRank to resolve conflicts and reduce text-to-SQL hallucinations.

An explanation of how the Genie Ontology method improves text-to-SQL accuracy by focusing on the underlying mechanism rather than the marketing pitch.

Proposes OPI, an ontology-guided framework for multi-hop knowledge graph question answering that leverages a relation-centric ontology graph for bidirectional retrieval and iterative refinement, achieving state-of-the-art results on multiple benchmarks.

This paper presents JD Oxygen AI Item Center (Oxygen AIIC), an industrial-scale platform leveraging LLMs/VLMs for item knowledge production and service, achieving high precision and recall, and delivering measurable gains in search, recommendation, and operations on JD.com.

TOTEN is a knowledge-based ontological tokenization framework that replaces statistical tokenization with declarative classification grounded in a formal ontology of engineering entities, achieving high ontological atomicity and numerical reconstruction for physical quantities and technical notation in Brazilian Portuguese.