Semantic Webs of Meaning

Equip your organization’s people and AI systems to interpret enterprise knowledge with greater precision, context, and accountability using knowledge graphs. This practical introduction helps data architects, AI engineers, governance leaders, technical executives, and subject matter experts create a shared semantic foundation using RDF, RDFS, OWL, SKOS, SHACL, SPARQL, ontologies, and linked data.Analyze why information scattered across databases, documents, code, catalogs, semantic layers, and people’s minds remains difficult to connect and reuse. Apply semantic modeling to define entities, classes, properties, identifiers, relationships, constraints, and business rules in forms that humans and machines can inspect. Clarify where formal ontologies complement relational databases, dimensional models, master data management, business intelligence, and existing data platforms.Create and query standards-based models with Turtle, Protégé, and Apache Jena Fuseki. Evaluate open-world reasoning, inference, validation, taxonomy design, vocabulary reuse, data virtualization, and provenance. Practical examples show how to formulate competency questions, write SPARQL queries, connect domain concepts, extract knowledge from existing sources, and ground large language models through retrieval-augmented generation.Scale from a focused proof of concept to a federated enterprise capability. Establish governance for ontology ownership, IRI management, versioning, security, lineage, reasoning, and cross-domain alignment. Compare graph technologies, examine infrastructure and development scalability, and account for the risks of inferred disclosures, conflicting definitions, unsupported conclusions, and automated decisions that cannot be explained.This book provides a realistic view of the value, effort, and discipline involved. Build the explicit, governed foundation that allows enterprise systems to move beyond retrieval and toward explanation, reasoning, and responsible decision support.