This blog post discusses the use of Large Language Models (LLMs) in conjunction with Neo4j to extract insights from unstructured data and convert it into a structured representation in the form of a knowledge graph. The three-step approach focuses on extracting nodes and relationships, entity disambiguation, and importing the data into Neo4j. Although there are challenges such as unpredictable output formatting, speed limitations, and the lack of accountability, the combination of LLMs and Neo4j offers a promising solution for unlocking the hidden value in unstructured data. The post provides valuable insights and practical knowledge for leveraging LLMs and Neo4j in knowledge extraction from unstructured data.
| Signal | Change | 10y horizon | Driving force |
|---|---|---|---|
| Construct Knowledge Graphs From Unstructured Text | Conversion of unstructured data to knowledge graphs | More efficient and accessible conversion process | Advancements in natural language processing |
| Extracting nodes and edges | Chunking and extracting entities | Improved chunking and labeling of entities | Overcoming limitations of LLMs |
| Entity disambiguation | Merging duplicate entities | More accurate consolidation of properties | Utilizing LLMs for entity merging |
| Importing the data into Neo4j | Transformation of LLM results into Neo4j format | Enhanced data import and preview capabilities | Compatibility with Neo4j database |
| Unpredictable output | Inherent nature of LLMs | Development of tools to parse LLM outputs | Improving LLM tooling and functionality |
| Speed | Slowness of the extraction process | Development of faster extraction approaches | Seeking more efficient extraction methods |
| Lack of accountability | Uncertainty in LLM decision-making | Improving data quality of knowledge graphs | Ensuring reliable and accurate extractions |