TrustGraph and Memgraph: Knowledge Retrieval for Complex Industries

Data silos in heavily regulated industries like aerospace and law hinder innovation by obscuring critical insights within vast, disconnected information such as compliance documents and technical standards. TrustGraph, an open-source framework developed by Daniel Davis and Mark Adams, addresses this challenge by integrating AI agents with knowledge graphs in Memgraph to transform unstructured data into connected knowledge, facilitating smarter decision-making at scale. TrustGraph's architecture, built on Apache Pulsar, ensures reliable, scalable data processing, while its RDF schema organizes data into a queryable framework, mitigating misinformation by revealing indirect connections. A demonstration showcased TrustGraph's ability to process complex datasets like UK legislation, highlighting its efficacy in handling large, intricate documents through chunking and knowledge graph structuring. Memgraph supports TrustGraph's operations by enabling efficient graph storage and querying, crucial for real-world applications in legal AI, compliance, and cybersecurity. The system optimizes data extraction by balancing generalization with customization, addressing challenges like noise and ensuring significant insights are accessible through precise querying.