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Knowledge Graph Engineering with Python
Last updated 8/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English + subtitle | Duration: 4h 55m | Size: 3.99 GB
Ontology based Knowledge Graph Assistant: Healthcare domain
What you'll learn
Build production-ready healthcare knowledge graph applications using Python, Neo4j, and enterprise knowledge graph engineering best practices.
Validate and maintain healthcare knowledge graphs using SHACL while ensuring data quality, semantic consistency, and integrity.
Develop REST APIs and backend services to query, manage, and integrate healthcare knowledge graphs with enterprise applications.
Design scalable semantic applications by combining healthcare ontologies, graph databases, semantic reasoning, and graph-based analytics.
Requirements
A basic understanding of RDF, OWL, SPARQL, and ontology engineering concepts. Completion of Part 1 is recommended but not mandatory.
Basic knowledge of Python programming, including variables, functions, and working with libraries.
Familiarity with graph databases or a willingness to learn Neo4j and Cypher during the course.
A computer running Windows, macOS, or Linux with permission to install free software such as Python, Neo4j Desktop, Docker (optional), and Visual Studio Code.
No prior experience with SHACL, enterprise knowledge graphs, or semantic application development is required. These concepts are taught step by step throughout the course.
Description
Enterprise AI systems require more than Large Language Models. They need structured knowledge, semantic understanding, reasoning capabilities, and explainable decision-making. This course teaches you how to build an Ontology-based Knowledge Graph Assistant for the Healthcare domain using modern Semantic Web technologies and enterprise AI engineering practices.
In this course, you will learn how to design healthcare ontologies, represent domain knowledge using RDF and OWL, model relationships between clinical entities, and build an intelligent Knowledge Graph that supports semantic querying and reasoning. You will also understand how ontology-driven knowledge representation enables AI systems to generate accurate, explainable, and context-aware responses for complex healthcare scenarios.
The course provides a practical, implementation-focused approach covering ontology modeling, RDF triples, OWL classes and properties, SPARQL querying, healthcare knowledge representation, semantic reasoning, and the integration of these technologies into an enterprise Knowledge Graph Assistant. Throughout the course, you will work with real-world healthcare examples and gain hands-on experience building an intelligent semantic system from scratch.
This course serves as the foundation for my advanced course on Enterprise Agentic AI and LangGraph-based multi-agent systems. Therefore, it is strongly recommended-and considered a prerequisite-that you first complete my course
Enterprise Ontology Engineering with Protégé, RDF & OWL
That course introduces the core concepts of ontology engineering, Semantic Web technologies, RDF, OWL, Protégé, and enterprise ontology design, which are extensively used throughout this Knowledge Graph Assistant course. Completing it first will help you understand the concepts more effectively and get the most value from this course.
Whether you are a Software Engineer, AI Engineer, Data Engineer, Knowledge Engineer, Solution Architect, Researcher, or a learner interested in Enterprise AI, Semantic Technologies, and Knowledge Graphs, this course will provide the practical skills required to build ontology-driven AI applications for real-world enterprise healthcare systems.
Who this course is for
AI Engineers, Data Engineers, and Solution Architects interested in integrating healthcare knowledge graphs into enterprise applications.
Knowledge Graph Engineers, Ontology Engineers, and Semantic Web Professionals looking to develop production-ready semantic solutions.
Software Engineers and Backend Developers who want to build enterprise knowledge graph applications using Python and Neo4j.
Researchers, Healthcare Informatics Professionals, and Graduate Students who want hands-on experience building scalable healthcare knowledge graph systems.
Anyone who has completed Part 1 (Enterprise Healthcare Ontology Engineering with Protégé, RDF, OWL & SPARQL) or has equivalent knowledge and wants to advance to enterprise knowledge graph engineering.
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