ontology

Helix Foundry 是一个基于 HelixDB 和 DuckDB 的本地优先数据工作台，可连接公司现有的数据库、Stripe、WorkOS、PostHog、文件和 API，并自动生成跨数据源的统一本体，全部在本地运行且默认使用本地 AI。

作者分享对 GraphRAG 的兴趣，并尝试构建一个轻量本体论、无需向量搜索的 Jev-based GraphRAG 变体。

The paper proposes a pipeline that automatically generates ontology-grounded, reasoner-verified multiple-choice benchmarks for evaluating LLM logical reasoning in scientific AI, validated on three ontologies (Pizza, PMDco, DOID) across six LLMs.

This paper introduces the Ontology-Based Contextual AI Evaluations (OB-CAIE) methodology, which uses Domain-Specific and Evaluation Process Ontologies to bring scientific rigor, traceability, and reproducibility to generative AI testing while balancing human expertise with automation.

EvoOntology introduces a self-evolving ontology layer for data agents, encapsulated as an MCP server, to bridge the agent-data gap and improve performance on heterogeneous data tasks as shown in benchmarks.

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.