Using LLMs to Create Knowledge Graphs From a Large Corpus of Parliamentary Debates

LLMs can effectively extract entities and relationships from text to create knowledge graphs, particularly excelling at entity recognition tasks

Knowledge graphs are ideal for representing data with many-to-many relationships between different types of entities, making querying and traversal more efficient than traditional databases

Graph databases store references between nodes upfront, making queries faster by eliminating the need for multiple joins, though write operations are slower

The RAG (Retrieval Augmented Generation) approach helps integrate private data with LLM capabilities by retrieving relevant context for queries

Validation and debugging of LLM-generated knowledge graphs remains challenging due to:

• Inconsistent outputs between runs
• Need for human verification
• Difficulty in constraining outputs
• Complex schema management

Parliamentary debate analysis revealed challenges in extracting consistent policy positions, as politicians discuss topics abstractly and change positions over time

Success depends heavily on high-quality prompts and clear instructions to the LLM

Post-processing and cleanup steps are often necessary to handle inconsistencies and ambiguous naming conventions

Graph representations provide more intuitive visualization and analysis of complex relationships between entities

Natural language interfaces (like text-to-Cypher queries) make knowledge graphs more accessible to non-technical users like journalists