Building a Knowledge Graph Ingestion/Retrieval Pipelines using Ontologies and Fine-tuning

Course Outline

Learn how to construct a knowledge graph (KG) pipeline for ingestion and retrieval using domain-specific ontologies, lightweight fine-tuned models, and practical engineering steps. This course includes an overview of tools like Neo4J, RDF triples, and LLM-based fact extraction, leading to the creation of a robust Retrieval-Augmented Generation (RAG) system.

Learning Outcomes

• Generate doman-specific ontologies
• Create training data and fine-tune a model
• Create an RAG ingestion/retrieval pipeline

Who Is This Course For?

• AI practitioners and engineers interested in retrieval systems.Developers with a passion for knowledge graphs and semantic technology.Beginners looking to explore RAG systems (basic Python knowledge recommended).

Pre-requisites

• Interest in RAG systems and AIPython engineering experience useful, but not required