Luiz Calaça
Software Engineer, Data Scientist and Professor
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Data Scientist, Software Engineer, Big Data and Machine Learning Specialist, SCRUM Professional Certificate, author of book chapters in the context of Data Science, NoSQL (Document Store, Graphs and RDF), Fuzzy Logic and Artificial Intelligence, Project Lead, Google Educator, Instructor in various technologies, University Professor of undergraduate and graduate courses, and, Robotics for children, National speaker, Consultant and Software Developer for over 10 years, immersed in the context of Innovation, Software, Internet of Things, Data Science, Data Warehouse, Business Intelligence and Artificial Intelligence (Machine and Deep Learning), Writer on brazilian sites: Embarcados, iMasters and on the Roboduca Blog, Solution Maker, studying MBA in Enterprise Management, specializing in Psychopedagogy and Neurosciences and graduating in the beautiful art of mathematics | www.luizcalaca.com.br | www.roboduca.com.br
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How to apply Semantic web on Neo4j
The representation of data (Big data) must be significant, because the purpose of storage is also the subsequent retrieval of information. When we look at semantic web and NoSQL databases as Neo4j we can modeling a domain with graphs and RDF and use ontologies to create a knowledge base. Let's learn concepts from semantic web and apply them on semantic graphs and see facilities of cypher to retrieval significant information.
Semantic Memory for LLMs: Graph-Based Retrieval and Reasoning with Node.js & Neo4j
Large Language Models excel at generating fluent text — but they still lack memory: the ability to store knowledge structurally, recall relationships, reason over connections, and explain how they arrived at an answer.
In this talk, we explore how Semantic Memory emerges when LLMs are combined with Knowledge Graphs, enabling deep contextual understanding and multi-hop reasoning impossible with traditional RAG.
Using Node.js + LangChain + LangGraph + Neo4j, we build an end-to-end Graph-Based Retrieval system that extracts entities and relations from unstructured data, stores them as a dynamic knowledge graph, and enables precise, explainable reasoning through GraphRAG-style pipelines.
Attendees will learn how graph-backed memory transforms LLMs from pattern generators into context-aware agents capable of semantic recall, logical inference, and enterprise-grade reliability.
Luiz Calaça
Software Engineer, Data Scientist and Professor
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