KGE-training: A Practical Project for Knowledge Graph Embedding Model Training and Visualization

KGE-training is an open-source knowledge graph embedding (KGE) model training project developed by eightTT. It completes the training and evaluation of TransE and RotatE models on the classic FB15K dataset, and visualizes the underlying reasoning structure learned by the models through t-SNE dimensionality reduction. This project provides a complete practical reference for beginners in the KGE field.

Knowledge graph embedding is a core technology in knowledge representation learning, which maps entities and relationships into a low-dimensional vector space while preserving semantic structure and reasoning capabilities. Traditional symbolic knowledge graphs are difficult to directly apply to machine learning, but embedding technology enables numerical representation, semantic preservation, reasoning capabilities, and support for downstream applications. This project provides practical cases for beginners.

FB15K Dataset

• Source: Freebase knowledge base
• Scale: 14,951 entities, 1,345 relationships, 483,142 training triples
• Features: Multi-domain, complex relationship patterns

TransE Model

• Core assumption: Relationships are translation operations between entities (h + r ≈ t)
• Scoring function: -||h + r - t|| (L1/L2 distance)
• Advantages and disadvantages: Simple and efficient, but struggles with complex relationships

RotatE Model

• Core innovation: Rotation operation in complex space (t = h ∘ r)
• Capabilities: Models symmetric, inverse, and compositional relationships
• Technique: Self-adversarial negative sampling to improve performance

Model Performance

• TransE: Performs well on one-to-one relationships, but performance drops on complex relationships
• RotatE: Overall better than TransE, with significant improvements in MRR and Hits@10

t-SNE Visualization

• Clustering phenomenon: Semantically similar entities form clusters
• Relationship patterns: Translation/rotation trajectories are visible
• Value: Verify model representations, detect anomalies, and understand reasoning

Knowledge graph embedding technology has a wide range of applications:

• Knowledge graph completion: Predict missing triples
• Intelligent question answering: Understand questions and retrieve answers
• Recommendation systems: Enhance recommendation effectiveness
• Semantic search: Retrieve based on semantic similarity
• Drug discovery: Predict drug-target relationships
• Financial risk control: Identify implicit corporate associations

Learning Path

• Basics: Knowledge graph concepts, vector embedding fundamentals, PyTorch/TensorFlow
• Intermediate: Understanding classic models, negative sampling and loss functions, dataset practice
• Advanced: Research on complex models, integration of KGE with LLM, application development

Resource Recommendations

• Papers: TransE (NIPS 2013), RotatE (ICLR 2019)
• Tools: LibKGE, OpenKE, DGL-KE
• Datasets: FB15K/FB15K-237, WN18/WN18RR, YAGO3-10

The KGE-training project provides a concise and complete practical case for KGE beginners. As a bridge connecting symbolic and vector representations, knowledge graph embedding is becoming increasingly important in the AI field. The current cutting-edge direction is the integration of KGE with large language models. This project serves as an entry-level reference and can support complex applications after improvements.