Constructing Knowledge Graphs From Unstructured Text Using LLMs

The text discusses the construction of knowledge graphs from unstructured text data using large language models (LLMs). The approach is based on three steps: extracting nodes and edges from the text, performing entity disambiguation to merge duplicate entities, and importing the data into a Neo4j database. LLMs are used in each step to automate the process, which can be time-consuming if done manually. The code for this project is available on GitHub. However, there are challenges associated with this approach, including unpredictable output formatting from the LLM, speed limitations, and potential lack of accountability. Despite these challenges, the three-step approach enables anyone to build knowledge graphs using LLMs and efficiently analyze large corpora of unstructured data.

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