Summary

This chapter described in detail how to create a knowledge graph, either using already structured data, such as an API result, or an existing knowledge graph that can be queried, such as Wikidata. We also learned how to use NLP and named entity recognition in order to extract information from unstructured data, such as a human-written text, and turn this information into a structured graph. We have also learned about two important applications of knowledge graphs: graph-based search, the method used by Google to provide even more accurate results to the users, and recommendations, which are a mandatory step for e-commerce today.

All of this was done with Cypher, extended by some plugins such as APOC or the NLP GraphAware plugin. In the rest of this book, we will make extensive use of another very important library when dealing with graph analytics: the Neo4j Graph algorithms library. The next chapter will introduce it and give application examples in the context of the shortest pathfinder challenges.