By the end of this course, learners will be able to:

1. Understand graph theory concepts relevant to data modeling and storage.
2. Explain the differences between graph, relational, and document databases.
3. Model entities and relationships using property and labeled graph structures.
4. Use Cypher, SPARQL, and Gremlin for graph querying and traversal.
5. Design ontology-driven schemas for building knowledge graphs.
6. Apply RDF and OWL for semantic modeling and linked data integration.
7. Perform graph analytics such as PageRank, community detection, and centrality.
8. Integrate graph databases with AI pipelines for reasoning and recommendations.
9. Optimize graph queries for performance and scalability in large datasets.
10. Deploy and visualize knowledge graphs using tools like Neo4j Bloom and GraphQL.

Course Syllabus

Module 1: Introduction to Graph Data Modeling
Understanding graph theory, nodes, relationships, and graph schema design.

Module 2: Types of Graph Databases
Property graphs vs RDF graphs; key differences and use cases.

Module 3: Query Languages for Graphs
Cypher, SPARQL, and Gremlin basics; writing and optimizing queries.

Module 4: Introduction to Neo4j and Its Ecosystem
Neo4j installation, modeling, data import, and Cypher practice.

Module 5: Graph Algorithms and Analytics
Traversal, PageRank, community detection, and pathfinding algorithms.

Module 6: RDF, Ontologies, and Semantic Modeling
Creating knowledge structures using RDF, RDFS, and OWL standards.

Module 7: Building Knowledge Graphs
Entity linking, relationship extraction, and schema integration from multiple sources.

Module 8: Graph Databases in AI and Machine Learning
Combining graphs with LLMs and embeddings for reasoning and retrieval.

Module 9: Graph APIs and Integration
Using GraphQL, REST, and Python drivers to connect and query graph systems.

Module 10: Governance, Security, and Data Lineage in Graphs
Access control, auditing, and maintaining trustworthy graph systems.

Module 11: Visualization and Analytics Tools
Using Neo4j Bloom, Gephi, and Power BI for graph visualization and analysis.

Module 12: Capstone Project – Building a Knowledge Graph
Design, implement, and deploy an enterprise knowledge graph integrating semantic data and AI reasoning layers.

Upon successful completion, learners will receive a Certificate of Excellence in Graph Databases & Knowledge Graphs from Uplatz (or equivalent institution).

This certification validates your proficiency in graph data modeling, querying, and semantic knowledge engineering. It signifies your capability to build connected, context-aware data systems that drive intelligent applications across industries.

The credential also demonstrates your understanding of both the technical aspects (database design, algorithms, and query optimization) and the conceptual layers (ontology modeling, semantics, and AI integration) of graph-based systems.

Holding this certificate signals to employers that you are equipped to:

• Design and implement scalable graph database solutions.
• Develop enterprise-grade knowledge graphs for AI and analytics.
• Contribute to data-driven innovation projects involving context, reasoning, and relationships.

It’s an essential recognition for professionals pursuing roles in Data Architecture, AI Engineering, Semantic Web Development, and Enterprise Knowledge Management.

Mastering Graph Databases & Knowledge Graphs opens opportunities in rapidly growing fields such as:

• Graph Data Engineer
• Knowledge Graph Developer
• Semantic Data Architect
• AI Knowledge Engineer
• Data Integration Specialist
• Ontology Engineer

These skills are highly valued in AI, finance, healthcare, retail, cybersecurity, and information retrieval domains—where connected data and reasoning drive innovation.

1. What is a graph database?
   A graph database is a data management system that stores data as nodes (entities) and edges (relationships) to model real-world connections efficiently.
2. How does a graph database differ from a relational database?
   Graph databases focus on relationships rather than tables and joins, offering faster queries for connected data.
3. What is the role of Cypher in Neo4j?
   Cypher is Neo4j’s declarative query language used to create, read, and traverse graph data structures.
4. What are knowledge graphs used for?
   Knowledge graphs represent relationships among entities to power search, recommendation, reasoning, and AI applications.
5. What is RDF and why is it important?
   RDF (Resource Description Framework) is a W3C standard for representing semantic data and enabling interoperability across systems.
6. Explain the difference between property graphs and RDF graphs.
   Property graphs store data in nodes and edges with attributes, while RDF graphs use triples (subject–predicate–object) to describe relationships.
7. What is an ontology in the context of knowledge graphs?
   An ontology defines the structure, vocabulary, and relationships within a knowledge domain.
8. What graph algorithms are commonly used in analytics?
   PageRank, shortest path, community detection, and centrality algorithms help reveal hidden patterns in data.
9. How are knowledge graphs integrated with AI?
   They provide structured context to AI systems, improving reasoning, retrieval, and explainability.
10. What are real-world use cases of graph databases?
    Fraud detection, social networks, supply chain optimization, recommendation engines, and enterprise data integration.