Ontology Engineering Pipeline

A practical framework for knowledge engineering, data modeling, and semantic systems design

von Eliana Tallis

192 Seiten, Taschenbuch

Kurzbeschreibung des Verlags

Build Smarter AI Systems by Engineering the Meaning Behind DataArtificial intelligence is only as powerful as the knowledge structures that support it. The Ontology Engineering Pipeline provides a practical framework for building semantic systems that help organizations transform disconnected data into reliable, machine-understandable knowledge.Inside this practical guide, you will discover how to: - Understand ontology engineering and its role in modern artificial intelligence, data modeling, and knowledge management.- Build a complete knowledge engineering pipeline from controlled vocabularies and metadata schemas to taxonomies, thesauri, ontologies, and enterprise knowledge graphs.- Improve AI performance by creating semantic foundations that support retrieval-augmented generation (RAG), semantic search, entity resolution, and AI governance.- Design meaningful data models that help different systems, teams, and applications understand the same concepts consistently.- Create machine-readable knowledge structures using concepts, relationships, properties, constraints, and reasoning models.- Develop enterprise knowledge graphs that connect information, improve discoverability, and enable smarter decision-making.- Apply governance and maintenance practices to ensure semantic systems remain accurate, scalable, and trustworthy over time.Perfect for: - Data engineers- AI engineers- Enterprise architects- Knowledge managers- Information scientists- Technology leaders building AI-ready organizationsWhether you are developing AI assistants, improving enterprise search, managing complex data environments, or building the next generation of intelligent systems, The Ontology Engineering Pipeline gives you the principles and practical methods needed to create reliable semantic infrastructure.Turn data into knowledge. Turn knowledge into intelligent systems.

• Reihe: Independently published
• ISBN: 9798174279124
• Sprache: Englisch
• Erscheinungsdatum: 14.09.2026
• Größe: 229 x 152 mm
• Verlag: Independently published