Natural language processing (NLP) is a domain of artificial intelligence that focuses on the processing of unstructured data, specifically textual data, to enable computers to understand and respond to human language. With 80-85% of business-relevant information originating from text format, computational linguistics and text analytics are essential for extracting meaningful information from large collections of textual data. Neo4j, a graph database platform, can connect bodies of text and establish context, making it suitable for NLP applications. By leveraging the power of graphs with Neo4j, elements of text can be stored as nodes, and connections between words are stored as relationships, allowing for efficient storage and analysis of textual data. The GraphAware library enables users to create a pipeline with various operations, such as tokenization, stop-words removal, and named-entity recognition, to process and annotate text data. With Neo4j, it is possible to build a knowledge graph by extracting information from raw text and tying the pieces together using links, enabling reasoners to derive new knowledge from the data. Additionally, NLP libraries in Python can be used to build a near-natural language querying feature on top of an existing graph database, such as Neo4j, by running an NLP pipeline on user input and utilizing the tokens to construct a Cypher query. This approach has limitations, but it demonstrates the potential for natural language search and knowledge graph construction using graph databases like Neo4j.