Semantic Webs of Meaning: Building Contextual Knowledge Graphs for Deduction and Integration, by Eugene Asahara
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.
Chapter 1: Introduction to Knowledge Graphs and Semantic Modeling
Chapter 2: Shared Semantic Resources
Chapter 3: Ontologies
Chapter 4: Knowledge Graphs vs. Traditional Data Structures
Chapter 5: LLMs as Implicit Knowledge Graphs
Chapter 6: Standards, Technologies, and Tools
Chapter 7: Querying and Reasoning
Chapter 8: Building and Scaling Knowledge Graphs
Chapter 9: Security Design Considerations
Chapter 10: Building and Growing an EKG
Appendix Overview
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.
Eugene Asahara, with a rich history of over 45 years in software development, including over 25 years focused on business intelligence, particularly SQL Server Analysis Services (SSAS), is currently working as a Principal Solutions Architect at Kyvos Insights. His exploration of knowledge graphs began in 2005 when he developed Soft-Coded Logic (SCL), a .NET Prolog interpreter designed to modernize Prolog for a data-distributed world. Later in 2012, Eugene ventured into creating Map Rock, a project aimed at constructing knowledge graphs that merge human and machine intelligence across numerous SSAS cubes. While these initiatives didn’t gain extensive adoption at the time, the lessons learned have proven invaluable. With the emergence of Large Language Models (LLMs), building and maintaining knowledge graphs has become practically achievable, and Eugene is leveraging his past experience and insights from SCL and Map Rock to this end. That experience is encapsulated in his books, Enterprise Intelligence and Time Molecules.
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