Tagged articles
ontology engineering
15 articles · Page 1 of 1
The article evaluates OWL2, SWRL, and SHACL specifications from cell engineering for enterprise semantic layer adoption, concluding that while the conceptual vocabulary is high-quality, direct deployment fails due to reasoning performance, open-world assumptions, and missing validation; it prescribes a full engineering overhaul including profile reduction, SWRL dismantling, SHACL construction, and governance processes.
OWL2SHACLSWRL
0 likes · 10 min read
This article compares implicit reasoning (OWL2/SWRL) with explicit reasoning (OntoL/Palantir), explaining why industrial knowledge graph systems prefer explicit reasoning for auditability, traceability, temporal/spatial reasoning, and performance in dynamic, open-world scenarios like manufacturing and intelligence analysis.
OWL2OntoLPalantir
0 likes · 13 min read
The article compares NVIDIA's Auto-Ontology, which uses LLMs to generate semantic layers from database schemas, with OntoL, which deterministically parses interface comments into DSL for rule-based reasoning, highlighting trade-offs between probabilistic flexibility and deterministic correctness for AI agent contexts.
AI AgentsAuto OntologyKnowledge Graphs
0 likes · 9 min read
The article argues that in ontology-driven enterprise systems, performance bottlenecks lie in data integration pipelines and business databases, not the graph database layer, using Palantir Foundry's architecture and Airbus Skywise's 1,000+ daily pipelines as evidence, and shows Memgraph's specs far exceed typical ontology loads.
Airbus SkywiseMemgraphPalantir
0 likes · 13 min read
NVIDIA open-sourced Auto Ontology, a tool that generates initial ontologies from metadata to give Data Agents reusable semantic context via MCP tools, cutting ontology creation time by 90% in vendor tests, though human certification and secure deployment remain essential for trusted, production-ready definitions.
Auto OntologyData AgentsEnterprise AI
0 likes · 19 min read
ZTE's O-R-R-A ontology framework and 璇玑 DataAgent address semantic interoperability gaps for AI agents through incremental ontology engineering and six runtime components, achieving 95% metric unification and minute-level risk identification across 10+ systems.
AI AgentsDataAgentO-R-R-A framework
0 likes · 5 min read
This article details a four-layer ontology engineering system that transforms heterogeneous business data into a real-time ontology, enabling read-only AI agents to perform multi-hop reasoning over stable object identities, class relationships, and natural-language rules — demonstrated through a traffic accident investigation case study.
AI AgentMulti-hop Reasoningbusiness semantics
0 likes · 25 min read
The article distinguishes objective and perspective ontologies, identifies two gaps between static knowledge and dynamic systems, and proposes a four-layer engineering paradigm—Unique Ontology, Perspective Ontology, Component-Based Design, and BIP—to build executable ontology twins for planning and optimization.
BIPComponent-Based EngineeringDigital Twin
0 likes · 27 min read
This article explains how large language models accelerate ontology engineering through candidate discovery, formalization, and validation, but cannot replace human responsibility for defining business objects, rules, and risk boundaries, proposing a seven-step human-AI collaboration process with three-zone isolation to prevent AI from polluting production baselines.
Business RulesGovernanceKnowledge Graphs
0 likes · 25 min read
This article reviews a 2026 paper on Graph Engineering for LLM agents, detailing the shift from individual agent intelligence to system intelligence via explicit task DAGs, runtime state management with checkpoints and replay, multi-agent coordination through capability modeling, and ontology engineering for shared semantics.
Agent CoordinationDAG SchedulingGraph Engineering
0 likes · 20 min read
The author details how LLMs failed in underwater battlefield simulations despite trying prompts, agents, RAG, and knowledge graphs, and explains why ontologies — formal, reasoning-capable world models — are essential for trustworthy AI decisions, illustrating with a concrete case and a six-step ontology engineering process.
AI decision-makingLLM limitationsOntoL
0 likes · 12 min read
This article analyzes ERP implementation failures through an ontology engineering lens, revealing how conceptual misalignment, logical inconsistencies, and physical migration risks derail projects, and proposes four principles: ontology-first design, 20% customization limit, master data governance, and phased domain evolution.
ERPImplementationMaster Data
0 likes · 14 min read
This article details a seven-step methodology to transform business research findings into verifiable business models ready for ontology engineering, using an order fulfillment risk case study to illustrate object identification, rule definition, evidence mapping, and validation through competency questions and boundary samples.
Business ModelingBusiness Rulescase study
0 likes · 28 min read
This article explains why ontology projects should define competency questions before modeling, detailing how these verifiable business questions constrain scope, expose data gaps, guide modeling trade-offs, and enable repeatable acceptance testing through traceability to data, rules, evidence, and test cases.
competency questionsknowledge representationmodel acceptance
0 likes · 22 min read
The article argues that the root cause of common AI deployment problems—poor RAG relevance, agent hallucinations, and brittle graph‑query code—is the lack of a unified semantic layer, and demonstrates how ontology engineering can supply a reasoning‑driven, adaptable contract that aligns concepts, constrains actions, and decouples business rules from implementation.
AI ArchitectureAgentRAG
0 likes · 9 min read
