Knowledge Graph
Definition: A knowledge graph is a way of organizing information so that AI can understand how different facts and entities connect to each other. It works like a map of relationships between people, places, laws, or concepts instead of just storing plain text.
Example
A legal knowledge graph could connect a case to the statutes it cites, the judges involved, and related rulings. When an AI uses this graph, it can trace those links to give more accurate, context-aware answers.
Why It Matters?
Knowledge graphs help legal AI systems think more like lawyers so that they understand relationships, not just words. They make it possible for AI to connect cases, laws, and parties in ways that mirror legal reasoning. This leads to more precise research, better document insights, and stronger compliance checks.
How to Implement?
To implement a knowledge graph, start by identifying the main entities in your data, such as cases, statutes, firms, and people. Use tools or databases that support graph structures like Neo4j or Amazon Neptune. Add relationships between entities: for example, “Case A cites Statute B.” Then connect your AI system so it can query the graph and retrieve linked information when answering legal questions.