Summary
In this chapter, we worked on transforming raw, semi-structured data into clean, normalized datasets, ready for integration into our knowledge graph. We then explored the best practices in graph modeling, focusing on how to structure your nodes and relationships to enhance search efficiency and ensure your graph remains scalable and performant. Following this, we tackled other Cypher techniques, equipping you with the skills to handle variable-length relationships, pattern matching, subqueries, and graph algorithms. You are now well prepared to build a knowledge graph-driven search that can handle even the most intricate data relationships.
In the next chapter, we will take a step further by exploring how to integrate Haystack into Neo4j. This practical guide will show you how to build powerful search functionalities within your knowledge graph, allowing you to leverage the full potential of both Neo4j and Haystack for intelligent search solutions.
