Document Object Model (DOM) Graph RAG

The DOM Graph RAG project introduces an innovative approach to Retrieval-Augmented Generation (RAG) in artificial intelligence, overcoming the limitations of traditional vector-based models. By leveraging Document Object Model (DOM) structures and knowledge graphs, it enhances the accuracy and reliability of AI-generated content, especially in high-stakes environments.Unlike vector-based systems that struggle with context loss, the DOM Graph RAG model preserves content integrity and enables multi-hop reasoning.

Utilizing the Darwin Information Typing Architecture (DITA) schema, it ensures effective retrieval and response generation.Moreover, the model incorporates neuro-symbolic reasoning, combining neural networks’ pattern recognition with logical reasoning. This allows for continuous updates to dynamic content, maintaining its relevance and accuracy. A sample chatbot showcases its ability to provide accurate, context-aware answers while reducing reliance on large language models, thus lowering computational costs.

Authors:
Michael Iantosca
Senior Director of Knowledge Platforms and Engineering
Avalara Inc.

Helmut Nagy
Chief Product Officer
Semantic Web Company GmbH

William Sandri
Data & Knowledge Engineer
Semantic Web Company GmbH