This article discusses the use of LLM (Language Model) and ontology to extract knowledge graphs from unstructured documents. It highlights the challenges of transforming unstructured data into a knowledge graph format and emphasizes the importance of using a specified ontology for generating a graph. The example of the Kennedy family tree is used to demonstrate the process. The article explores different prompts and instructions for ChatGPT to accurately convert text into an RDF graph conforming to the provided ontology. The conversation with ChatGPT reveals the significance of using consistent terminology and precise prompts. It also discusses the benefits of knowledge graphs and suggests future applications and possibilities for their use.
| Signal | Change | 10y horizon | Driving force |
|---|---|---|---|
| LLM Ontology-prompting for Knowledge Graph Extraction | Extracting a graph from unstructured documents using an LLM prompted with an ontology | More efficient and accurate extraction of knowledge graphs from unstructured text | Enhancing data organization and analysis capabilities |
| ETL/ELT from unstructured text to graph is challenging | Overcoming challenges of transforming unstructured text to a graph format | Improved algorithms and technologies for ETL/ELT processes | Increasing demand for structured data analysis |
| Using LLM to generate a graph with a specified ontology | Generating a graph that adheres to a specified ontology/schema | More precise and structured graphs based on specific ontologies | Ensuring data consistency and compatibility with existing systems |
| RDF ontology defining the schema for the Kennedy family | Defining the schema and relationships within the Kennedy family using RDF ontology | Enhanced understanding and representation of the Kennedy family tree | Preservation and organization of historical and genealogical data |
| Pedantic prompting for accurate graph generation | Crafting precise prompts for LLM to generate accurate graphs | Improved accuracy and relevance of generated graphs | Ensuring the desired output and minimizing errors |
| Semantic verification of the generated graph | Verifying the semantic correctness of the generated graph | Increased confidence in the accuracy and reliability of the graph | Ensuring data integrity and credibility |
| Knowledge Graphs as a model for data | Utilizing Knowledge Graphs as a comprehensive data model | Wider adoption and utilization of Knowledge Graphs for data representation | Leveraging structured data for various applications and analysis |