“Google” Your Own Brain: Create a CMS with Neo4j & Elasticsearch [Community Post]

The Grasp Theory project is exploring a new way to catalogue and recall documents that are personally relevant, leveraging Neo4j. Having a graph to represent connections between content is powerful, like Google's PageRank algorithm, but doesn't necessarily mean "relevant" in the context of personal relevance. To address this, the team is using Mazerunner, which integrates an existing Neo4j database with Apache Spark and GraphX to generate graph analytics like PageRank, enhancing the relevancy of searches. By adding PageRank values to nodes in Neo4j and re-indexing them into Elasticsearch, users can tweak search results, potentially providing a more personalized experience. The project is ongoing, with plans to import more data, explore additional graph analytics algorithms, and consider "priming" users' brains for better relevance.