Knowledge Graphs for AI and Data Science Training Course

 Knowledge Graphs for AI and Data Science Training Course 

Introduction 

Knowledge Graphs for AI and Data Science Training Course provides comprehensive practical knowledge and technical skills in designing, developing, implementing, and managing knowledge graphs for modern artificial intelligence (AI), machine learning, and data science applications. The course explores knowledge representation, semantic data modelling, ontology engineering, graph databases, Resource Description Framework (RDF), Web Ontology Language (OWL), SPARQL querying, graph analytics, and entity resolution. Participants will learn how knowledge graphs connect structured and unstructured data, improve data interoperability, strengthen contextual understanding, and support intelligent information retrieval. The training also examines how knowledge graphs integrate with large language models (LLMs), generative AI, retrieval-augmented generation (RAG), and enterprise data platforms to deliver accurate, explainable, and context-aware AI solutions. 

The course emphasizes practical knowledge graph development, semantic search, graph-based machine learning, and scalable data architecture for enterprise AI transformation. Participants will explore industry applications involving healthcare, financial services, telecommunications, e-commerce, cybersecurity, and supply chain management. Through practical exercises and global case studies, learners will understand how organizations leverage graph technologies to improve decision-making, detect complex relationships, automate knowledge discovery, and enhance predictive analytics. The programme also covers knowledge graph governance, graph visualization, data quality, privacy, and performance optimization, equipping participants with the competencies required to build production-ready knowledge graph solutions for advanced AI and data science environments. 

Course Objectives 

• Understand knowledge graphs and semantic technologies for AI.
• Develop expertise in ontology engineering and knowledge representation.
• Design scalable graph data models and architectures.
• Implement RDF, OWL, and SPARQL technologies.
• Integrate knowledge graphs with machine learning workflows.
• Develop graph-based AI and predictive analytics solutions.
• Apply knowledge graphs to generative AI and large language models.
• Implement retrieval-augmented generation using graph data.
• Perform entity resolution and relationship extraction.
• Apply graph algorithms and network analysis.
• Strengthen data interoperability and semantic integration.
• Implement knowledge graph governance and quality management.
• Optimize graph databases for enterprise AI applications.

Organizational Benefits
 

• Improved enterprise data integration and interoperability.
• Enhanced AI accuracy through contextual knowledge.
• Better decision-making using relationship analytics.
• Reduced information silos and data duplication.
• Improved fraud detection and risk intelligence.
• More effective enterprise knowledge management.
• Enhanced generative AI and retrieval-augmented generation.
• Improved predictive analytics and machine learning.
• Stronger data governance and traceability.
• Increased innovation through intelligent data ecosystems.

Target Audiences
 

• Data scientists and machine learning engineers.
• Artificial intelligence engineers and developers.
• Data engineers and database administrators.
• Knowledge engineers and ontology specialists.
• Business intelligence and analytics professionals.
• Enterprise architects and IT managers.
• Research scientists and academic professionals.
• Digital transformation and data governance leaders.

Course Duration: 5 days

Course Modules

Module 1: Introduction to Knowledge Graphs and Semantic Technologies
 

• Fundamentals of knowledge graphs and semantic data.
• Knowledge representation, entities, relationships, and properties.
• Differences between relational databases and graph databases.
• Applications of knowledge graphs in AI and data science.
• Knowledge graph architecture and ecosystem components.
• Case study: Google's Knowledge Graph and intelligent search.

Module 2: Knowledge Representation and Ontology Engineering
 

• Ontology fundamentals and semantic modelling principles.
• Developing taxonomies, vocabularies, and domain ontologies.
• Resource Description Framework (RDF) and RDF Schema.
• Web Ontology Language (OWL) and reasoning mechanisms.
• Ontology validation, reuse, and interoperability.
• Case study: SNOMED CT for healthcare knowledge representation.

Module 3: Graph Databases and Knowledge Graph Development
 

• Property graphs, nodes, edges, and graph properties.
• Introduction to Neo4j and graph database architecture.
• Knowledge graph construction and schema development.
• Graph querying using Cypher and SPARQL.
• Graph storage, indexing, and performance optimization.
• Case study: LinkedIn's use of graph technologies for professional connections.

Module 4: Data Integration, Entity Resolution and Knowledge Extraction
 

• Integrating structured, semi-structured, and unstructured data.
• Entity recognition and relationship extraction techniques.
• Data cleansing, entity matching, and deduplication.
• Natural language processing for knowledge extraction.
• Automated knowledge graph construction pipelines.
• Case study: Financial institutions applying entity resolution for fraud detection.

Module 5: Knowledge Graphs for Machine Learning and Data Science
 

• Graph analytics and network science fundamentals.
• Graph embeddings and representation learning.
• Graph Neural Networks (GNNs) and predictive modelling.
• Link prediction, node classification, and clustering.
• Graph-based recommendation and anomaly detection.
• Case study: Pinterest's graph-based recommendation ecosystem.

Module 6: Knowledge Graphs for Generative AI and Large Language Models
 

• Integrating knowledge graphs with large language models.
• Graph-based retrieval-augmented generation architectures.
• Semantic search and context-aware information retrieval.
• Reducing hallucinations through grounded knowledge retrieval.
• Building intelligent question-answering applications.
• Case study: Microsoft GraphRAG for knowledge-intensive information discovery.

Module 7: Knowledge Graph Visualization, Governance and Security
 

• Graph visualization and interactive exploration techniques.
• Knowledge graph quality assessment and validation.
• Data lineage, metadata management, and governance.
• Access control, privacy, and knowledge graph security.
• Monitoring, maintenance, and lifecycle management.
• Case study: Healthcare knowledge graph governance for connected patient information.

Module 8: Enterprise Implementation and Advanced Applications
 

• Designing scalable enterprise knowledge graph architectures.
• Cloud-based knowledge graph deployment strategies.
• Integrating graph databases with enterprise data platforms.
• Knowledge graph applications in cybersecurity and supply chains.
• Production deployment, evaluation, and optimization.
• Case study: Amazon product knowledge graphs for e-commerce discovery.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical knowledge graph modelling and ontology development exercises.
• Hands-on demonstrations using Neo4j, RDF, OWL, and SPARQL.
• Group projects involving AI and data science applications.
• Global industry case studies and collaborative problem-solving.
• Practical assessments, project presentations, and knowledge evaluations.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.
 
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
 
c. Course duration is flexible and the contents can be modified to fit any number of days.
 
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
 
e. One-year post-training support Consultation and Coaching provided after the course.
 
f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.