Knowledge Graph Construction Demo from raw text using an LLM
Science & Technology
Introduction
In a recent project called "na llm," software engineer Noah Mayroffer at Neo4j explores the integration of larger range models with Neo4j to identify potential use cases. The project includes creating various demos, with one particular demo focusing on generating a knowledge graph from unstructured data. This demo, named "unstructured import," aims to showcase the capabilities of leveraging text data to create a knowledge graph. In this demonstration, the Wikipedia article for the James Bond franchise is used as an example to highlight the strengths and limitations of the demo.
The demo interface offers options to toggle schema usage, search for a file, or proceed with the import process. By selecting the Wikipedia article text file, the import process begins, converting the unstructured data into a knowledge graph. After the import completes, the resulting graph can be saved in different formats for further analysis. By utilizing the power of LLM (Large Language Models) and Neo4j, this demonstration illustrates the efficient creation of a knowledge graph from raw text data.
Keywords:
- LLM
- Knowledge Graph
- Unstructured Data
- Neo4j
- Demonstration
- Text Analysis
FAQ:
What is the main goal of the "unstructured import" demo showcased in the article? The main goal of the demo is to demonstrate how to create a knowledge graph from unstructured data, specifically using the James Bond Wikipedia article as an example.
How does the demo use LLM and Neo4j to achieve its objectives? The demo leverages Large Language Models (LLM) and Neo4j to process unstructured text data, extract relevant information, and generate a structured knowledge graph based on the input.
What are some of the strengths and weaknesses highlighted during the demo? The strengths of the demo include efficiently converting text data into a structured graph while the weaknesses may involve occasional inaccuracies in relationships and node categorization within the resulting knowledge graph.