The author of the text is exploring the concept of a decentralized finance (DeFi) knowledge graph using Neo4j, a graph database. The goal is to learn how the Sovryn protocol works and obtain a robust dataset about it. The author defines several node types for the graph, including Block, Transaction, Address, Token, Contract, and LogEvent. They then create a query system that allows users to interact with the graph using Python. The author demonstrates how to load data from the protocol directly into Python and perform various queries to analyze the data, such as finding high-value transactions and analyzing swaps between tokens. The text concludes by highlighting the potential of knowledge graphs to answer any question related to DeFi and mentions the possibility of applying this approach to other blockchains like Ethereum.