The text discusses the implementation of an enterprise knowledge graph using Google Drive and Neo4j. The knowledge graph is designed to connect discrete pieces of information together with context, providing fast and flexible querying capabilities. It leverages AI/ML-based predictive capabilities to provide relevant suggestions and answers in real-time. The system extracts metadata from Google Drive documents and ingests them into Neo4j, creating a graph that can be queried and analyzed. The knowledge graph is extended by adding n-grams, which are used to calculate document similarity and identify key terms. The system provides insights into the connectedness of documents, popularity of certain words/phrases, and similarity between documents based on n-gram overlap and position in the hierarchy.