%0Computer Program
%TKnowledge Oriented Graph Unified Transformer (KOGUT) v0.1
%XKOGUT — Knowledge Oriented Graph Unified Transformer

KOGUT implements the Relational Graph Transformer (RelGT) architecture for knowledge graph link prediction in biological domains, with a primary focus on microbial growth media prediction. While the original RelGT (arXiv:2505.10960) targets relational tables, time series, and multi-table databases, KOGUT adapts this architecture for heterogeneous biological knowledge graphs, providing first-in-class AI predictive models for microbial cultivation.

Key Adaptations Beyond Original RelGT:
- Knowledge Graph Focus: Applied to biological KGs with semantic node types (taxa, chemicals, media, phenotypes, environments) versus generic relational database tables, trained on the KG-Microbe knowledge graph (1.3M entities, 2.9M edges, 24 relation types).
- Multimodal Node Encoding: Integrates node labels, categories, descriptions, and synonyms from KG metadata through learned embedding layers—adapting relational column features to graph node attributes with textual semantics.
- Extended K-Hop Subgraph Strategy: Optimized neighborhood sampling (3-hop default, configurable up to 200 nodes) tuned for sparse biological networks, building on the original local-global attention framework with biological relation preservation.
- Biolink Predicate Preservation: Type-specific transformations for 24 biological edge semantics (occurs_in, consumes, produces, has_phenotype, subclass_of) beyond standard relational foreign keys, enabling multi-relation link prediction.
- Inductive Learning Support: Enables zero-shot predictions for novel taxa through feature-based embeddings (temperature, oxygen requirements, gram stain, cell shape), extending the original transductive relational benchmark scope to uncultured microorganisms.

CheapSOTA Performance Optimizations (This Distribution):
- VQ-EMA Centroid Attention: Vector quantization with exponential moving average for improved global context modeling (+5-10% MRR improvement).
- HDF5 Precomputed Data Loading: One-time preprocessing of k-hop subgraphs to eliminate redundant graph traversals (2-5× training speedup).
- Distributed Data Parallel Training: Multi-GPU support for scaling to larger knowledge graphs (tested on 4× NVIDIA A100 GPUs at NERSC Perlmutter).
- Mixed Precision Training: Automatic mixed precision (AMP) for memory efficiency and faster training.

Advantages Over Standard Knowledge Graph Embedding Models:
Combines RelGT's proven multi-element tokenization (features, type, hop, structure) with graph-native biological representations, enabling interpretable link prediction across heterogeneous entities that standard embedding models (TransE, RotatE, ComplEx) and table-based transformers cannot directly model. Achieves near-perfect performance on microbial growth media prediction (MRR: 0.9966, Precision@1: 0.9932, Hit@10: 1.0000) while maintaining explainability through attention-based reasoning over biological pathways.

Training Data:
- KG-Microbe merged knowledge graph: 1,379,337 nodes, 2,960,472 edges
- 24 biological relation types including taxonomic hierarchies, metabolic interactions, phenotype associations, and environmental relationships
- Primary prediction task: Growth media suitability for microbial taxa (biolink:occurs_in, 50K edges)
- Multi-relation capability: Predicts links for any of the 24 relation types, including chemical consumption/production, phenotype associations, and taxonomic classification

Citation:
Original RelGT Architecture:
  Dwivedi et al., "Relational Graph Transformer", arXiv:2505.10960, 2025

KOGUT Implementation:
  Knowledge Oriented Graph Unified Transformer for Microbial Growth Media Prediction
  Developed at Lawrence Berkeley National Laboratory (LBNL)
  Trained on NERSC Perlmutter supercomputer
%AJoachimiak, Marcin
%Rhttps://doi.org/10.11578/dc.20260210.3
%Uhttps://www.osti.gov/doecode/biblio/175162
%CUnited States
%D2025
%GEnglish
%2USDOE
%1AC02-05CH11231
2025-12-18