NAKG

AI Research • January 12, 2025

A breakthrough Neural Adaptive Knowledge Graph system designed for complex medical billing scenarios. Creates dynamic connections between documents with perfect F1 scores and 62% higher recall on complex queries.

Machine Learning
NAKG

Key Features

  • •Neural Adaptive Knowledge Graph architecture
  • •Edge Transformer for dynamic relationship learning
  • •Hierarchical document clustering
  • •Advanced medical document processing
  • •Complex relationship detection
  • •F1 scores of 0.982±0.015 on knowledge graph tasks
  • •62.0% higher recall on complex queries

NAKG (Neural Adaptive Knowledge Graphs) is a breakthrough in document understanding, specifically designed for complex medical billing scenarios. The system creates dynamic connections between documents, similar to how human experts process related medical information.

Core Components

  1. Edge Transformer: Dynamic relationship identification between documents
  2. Neural Gossip Network: Information sharing between related documents
  3. Hierarchical Clustering: Efficient processing of large document sets

Performance Highlights

  • Perfect scores on knowledge graph queries (1.000 F1)
  • 62.0% higher recall on complex queries
  • Temporal reasoning performance: F1 0.978±0.012
  • Human-like relationship detection

Implementation

The system uses a three-phase training process:

  1. Pre-training on medical documents
  2. Relationship learning
  3. End-to-end fine-tuning

Currently deployed at Backwork for medical billing automation, processing millions of documents with state-of-the-art accuracy.