| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ~Get Your Files Here ! | |||
| 1 - Introduction | |||
| 1 - Glossary-Part2.pdf | 82.7 KB | ||
| 1 - Introduction.mp4 | 38.3 MB | ||
| 1 - Project Overview_ Part2.pdf | 290.8 KB | ||
| 1 - SPARQL and CYPHER Cookbook.pdf | 388.1 KB | ||
| 1 - Softwares and Hardware Requirements.pdf | 74.1 KB | ||
| 10 - Embedding Service for querying Natural language | |||
| 11 - Embedding Service for querying Natural language.mp4 | 132 MB | ||
| 11 - Natural Language to SPARQL generation | |||
| 12 - Natural Language to Cypher | |||
| 13 - SHACL Validation | |||
| 14 - Observability and Deployment | |||
| 16 - Observability and Deployment.mp4 | 209.9 MB | ||
| 2 - From Chatbots to Knowledge-Driven Agentic AI Why Semantic Technologies Matter | |||
| 2 - Installing Neo4j Plugins for Semantic AI Development.pdf | 112.8 KB | ||
| 2 - Knowledge Graph Databases comparison.png | 1.6 MB | ||
| 2 - Part 2-Module 1_ Quick Revisions and Interview Questions.pdf | 62 KB | ||
| 2 - Why Semantic Technologies Matter.mp4 | 145.5 MB | ||
| 3 - Github repo structure of the Project | |||
| 3 - Cypher Queries Interview Questions.pdf | 69.1 KB | ||
| 3 - Github repo structure.mp4 | 139.7 MB | ||
| 4 - System design of the Project | |||
| 4 - Part 2- System Design_ Quick Revisions and Interview Questions.pdf | 65 KB | ||
| 4 - System design of the project.mp4 | 157.6 MB | ||
| 5 - Conversion of RDF to Property Graph | |||
| 5 - Conversion of RDF to Property Graph.mp4 | 152.7 MB | ||
| 5 - Part 2- Convert RDF to Property Files_ Quick Revisions and Interview Questions.pdf | 60.4 KB | ||
| 5 - Part 2-Module 3_ Quick Revisions and Interview Questions.pdf | 70.8 KB | ||
| 6 - Generate Ontology through Python file and add more individuals into the Ontology | |||
| 6 - Generate Ontology through Python file and add more individuals into the Ontology.mp4 | 193.7 MB | ||
| 6 - Part 2- Generate Ontology thru Python files_ Quick Revisions and Interview Questions.pdf | 70.8 KB | ||
| 7 - Load Ontology into neo4j Graph | |||
| 7 - Load Ontology into neo4j Graph.mp4 | 133.6 MB | ||
| 7 - Loading Ontology into Neo4j.pdf | 100.1 KB | ||
| 8 - API endpoints used in the Project | |||
| 8 - API endpoints used in the Project.mp4 | 304.9 MB | ||
| 8 - APIs.pdf | 117.8 KB | ||
| 9 - Exporting graph into Neo4j | |||
| 10 - Exporting graph into neo4j.mp4 | 132.2 MB | ||
| 9 - Exporting graph into Neo4j.mp4 | 137.3 MB | ||
| 15 - SHACL Validation.mp4 | 233.7 MB | ||
| 15 - SHACL Validation.pdf | 93.5 KB | ||
| 14 - Natural Language to Cypher.mp4 | 151.6 MB | ||
| 13 - Natural Language to SPARQL generation.mp4 | 128.7 MB | ||
| 12 - Embedding Service for querying Natural language.mp4 | 147.9 MB |
Knowledge Graph Engineering with Python
https://WebToolTip.com
Published 7/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 3h 7m | Size: 2.48 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.
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Udemy - Life Insurance Simplified Using Industry Knowledge Structure Posted by
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