gh-k-dense-ai-claude-scientific-skills-scientific-skills/skills/torchdrug/references/knowledge_graphs.md

Knowledge Graph Reasoning

Overview

Knowledge graphs represent structured information as entities and relations in a graph format. TorchDrug provides comprehensive support for knowledge graph completion (link prediction) using embedding-based models and neural reasoning approaches.

Available Datasets

General Knowledge Graphs

FB15k (Freebase subset):

• 14,951 entities
• 1,345 relation types
• 592,213 triples
• General world knowledge from Freebase

FB15k-237:

• 14,541 entities
• 237 relation types
• 310,116 triples
• Filtered version removing inverse relations
• More challenging benchmark

WN18 (WordNet):

• 40,943 entities (word senses)
• 18 relation types (lexical relations)
• 151,442 triples
• Linguistic knowledge graph

WN18RR:

• 40,943 entities
• 11 relation types
• 93,003 triples
• Filtered WordNet removing easy inverse patterns

Biomedical Knowledge Graphs

Hetionet:

• 45,158 entities (genes, compounds, diseases, pathways, etc.)
• 24 relation types (treats, causes, binds, etc.)
• 2,250,197 edges
• Integrates 29 public biomedical databases
• Designed for drug repurposing and disease understanding

Task: KnowledgeGraphCompletion

The primary task for knowledge graphs is link prediction - given a head entity and relation, predict the tail entity (or vice versa).

Task Modes

Head Prediction:

• Given (?, relation, tail), predict head entity
• "What can cause Disease X?"

Tail Prediction:

• Given (head, relation, ?), predict tail entity
• "What diseases does Gene X cause?"

Both:

• Predict both head and tail
• Standard evaluation protocol

Evaluation Metrics

Ranking Metrics:

• Mean Rank (MR): Average rank of correct entity
• Mean Reciprocal Rank (MRR): Average of 1/rank
• Hits@K: Percentage of correct entities in top K predictions
  • Typically reported for K=1, 3, 10

Filtered vs Raw:

• Filtered: Remove other known true triples from ranking
• Raw: Rank among all possible entities
• Filtered is standard for evaluation

Embedding Models

Translational Models

TransE (Translation Embedding):

• Represents relations as translations in embedding space
• h + r ≈ t (head + relation ≈ tail)
• Simple and effective baseline
• Works well for 1-to-1 relations
• Struggles with N-to-N relations

RotatE (Rotation Embedding):

• Relations as rotations in complex space
• Better handles symmetric and inverse relations
• State-of-the-art on many benchmarks
• Can model composition patterns

Semantic Matching Models

DistMult:

• Bilinear scoring function
• Handles symmetric relations naturally
• Cannot model asymmetric relations
• Fast and memory efficient

ComplEx:

• Complex-valued embeddings
• Models asymmetric and inverse relations
• Better than DistMult for most graphs
• Balances expressiveness and efficiency

SimplE:

• Extends DistMult with inverse relations
• Fully expressive (can represent any relation pattern)
• Two embeddings per entity (canonical and inverse)

Neural Logic Models

NeuralLP (Neural Logic Programming):

• Learns logical rules through differentiable operations
• Interprets predictions via learned rules
• Good for sparse knowledge graphs
• Computationally more expensive

KBGAT (Knowledge Base Graph Attention):

• Graph attention networks for KG completion
• Learns entity representations from neighborhood
• Handles unseen entities through inductive learning
• Better for incomplete graphs

Training Workflow

Basic Pipeline

from torchdrug import datasets, models, tasks, core

# Load dataset
dataset = datasets.FB15k237("~/kg-datasets/")

# Define model
model = models.RotatE(
    num_entity=dataset.num_entity,
    num_relation=dataset.num_relation,
    embedding_dim=2000,
    max_score=9
)

# Define task
task = tasks.KnowledgeGraphCompletion(
    model,
    num_negative=128,
    adversarial_temperature=2,
    criterion="bce"
)

# Train with PyTorch Lightning or custom loop

Negative Sampling

Strategies:

• Uniform: Sample entities uniformly at random
• Self-Adversarial: Weight samples by current model's scores
• Type-Constrained: Sample only valid entity types for relation

Parameters:

• num_negative: Number of negative samples per positive triple
• adversarial_temperature: Temperature for self-adversarial weighting
• Higher temperature = more focus on hard negatives

Loss Functions

Binary Cross-Entropy (BCE):

• Treats each triple independently
• Balanced classification between positive and negative

Margin Loss:

• Ensures positive scores higher than negative by margin
• max(0, margin + score_neg - score_pos)

Logistic Loss:

• Smooth version of margin loss
• Better gradient properties

Model Selection Guide

By Relation Patterns

1-to-1 Relations:

• TransE works well
• Any model will likely succeed

1-to-N Relations:

• DistMult, ComplEx, SimplE
• Avoid TransE

N-to-1 Relations:

• DistMult, ComplEx, SimplE
• Avoid TransE

N-to-N Relations:

• ComplEx, SimplE, RotatE
• Most challenging pattern

Symmetric Relations:

• DistMult, ComplEx
• RotatE with proper initialization

Antisymmetric Relations:

• ComplEx, SimplE, RotatE
• Avoid DistMult

Inverse Relations:

• ComplEx, SimplE, RotatE
• Important for bidirectional reasoning

Composition:

• RotatE (best)
• TransE (reasonable)
• Captures multi-hop paths

By Dataset Characteristics

Small Graphs (< 50k entities):

• ComplEx or SimplE
• Lower embedding dimensions (200-500)

Large Graphs (> 100k entities):

• DistMult for efficiency
• RotatE for accuracy
• Higher dimensions (500-2000)

Sparse Graphs:

• NeuralLP (learns rules from limited data)
• Pre-train embeddings on larger graphs

Dense, Complete Graphs:

• Any embedding model works well
• Choose based on relation patterns

Biomedical/Domain Graphs:

• Consider type constraints in sampling
• Use domain-specific negative sampling
• Hetionet benefits from relation-specific models

Advanced Techniques

Multi-Hop Reasoning

Chain multiple relations to answer complex queries:

• "What drugs treat diseases caused by gene X?"
• Requires path-based or rule-based reasoning
• NeuralLP naturally supports this

Temporal Knowledge Graphs

Extend to time-varying facts:

• Add temporal information to triples
• Predict future facts
• Requires temporal encoding in models

Few-Shot Learning

Handle relations with few examples:

• Meta-learning approaches
• Transfer from related relations
• Important for emerging knowledge

Inductive Learning

Generalize to unseen entities:

• KBGAT and other GNN-based methods
• Use entity features/descriptions
• Critical for evolving knowledge graphs

Biomedical Applications

Drug Repurposing

Predict "drug treats disease" links in Hetionet:

1. Train on known drug-disease associations
2. Predict new treatment candidates
3. Filter by mechanism (gene, pathway involvement)
4. Validate predictions experimentally

Disease Gene Discovery

Identify genes associated with diseases:

1. Model gene-disease-pathway networks
2. Predict missing gene-disease links
3. Incorporate protein interactions, expression data
4. Prioritize candidates for validation

Protein Function Prediction

Link proteins to biological processes:

1. Integrate protein interactions, GO terms
2. Predict missing GO annotations
3. Transfer function from similar proteins

Common Issues and Solutions

Issue: Poor performance on specific relation types

• Solution: Analyze relation patterns, choose appropriate model, or use relation-specific models

Issue: Overfitting on small graphs

• Solution: Reduce embedding dimension, increase regularization, or use simpler models

Issue: Slow training on large graphs

• Solution: Reduce negative samples, use DistMult for efficiency, or implement mini-batch training

Issue: Cannot handle new entities

• Solution: Use inductive models (KBGAT), incorporate entity features, or pre-compute embeddings for new entities based on their neighbors

Best Practices

1. Start with ComplEx or RotatE for most tasks
2. Use self-adversarial negative sampling
3. Tune embedding dimension (typically 500-2000)
4. Apply regularization to prevent overfitting
5. Use filtered evaluation metrics
6. Analyze performance per relation type
7. Consider relation-specific models for heterogeneous graphs
8. Validate predictions with domain experts