Crafting a Neo4j Knowledge Graph Semantic Reasoner for LPG-Based Ontologies with Python and CDC

Session Track: Knowledge Graphs

Session Time: 12:30 pm - 01:00 pm November 6

Session description

Knowledge graphs offer robust solutions for modeling complex relationships, yet developing dynamic, reasoning-driven applications with ontology-like semantics remains a significant challenge. In this session, Pierre Halftermeyer will guide you through constructing a Neo4j-based semantic reasoner using Python, utilizing Change Data Capture (CDC) for real-time updates, and will propose a labeled property graph (LPG) equivalent to RDF ontologies. You will explore key concepts such as semantic reasoning, graph modeling, CDC integration, and ontology mapping while leveraging Cypher and Python. By the end, you will learn to design and implement a scalable knowledge graph reasoner, process dynamic data changes, map LPG to ontological structures, and apply semantic inference to address real-world problems effectively.