The author of this text is attempting to link data from multiple sources, specifically creating relationships between nodes in a graph database, Neo4j. To accomplish this, they utilize the FuzzyWuzzy Python package for string matching and comparison. The goal is to create meaningful connections between entities, such as drug firms and drugs, or legislators and their information, by identifying similarities in their names. The author preprocesses the strings to remove duplicates and non-alphanumeric characters, sorts them, and then applies FuzzyWuzzy's partial ratio and ratio functions to determine the matching rate. They also introduce a modification step to exclude false positives by filtering common words from the strings. Ultimately, they create relationships between nodes using Cypher queries, storing the confidence level of the match as properties for each relationship. The author hopes this post will be helpful to those facing similar challenges in connecting data in graph databases.