# TransG : A Generative Mixture Model for Knowledge Graph Embedding

arXiv: https://arxiv.org/abs/1509.05488 · HTML: https://mlsanity.com/paper/1509.05488/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2015.

## Built on by (most cited first, 28 of 28)

- [Exploring Large Language Models for Knowledge Graph Completion](https://mlsanity.com/paper/2308.13916/) (2023)
- [A Benchmark and Comprehensive Survey on Knowledge Graph Entity Alignment via Representation Learning](https://mlsanity.com/paper/2103.15059/) (2021)
- [How to Turn Your Knowledge Graph Embeddings into Generative Models](https://mlsanity.com/paper/2305.15944/) (2023)
- [Geometry Interaction Knowledge Graph Embeddings](https://mlsanity.com/paper/2206.12418/) (2022)
- [Contextualization Distillation from Large Language Model for Knowledge Graph Completion](https://mlsanity.com/paper/2402.01729/) (2024)
- [Link Prediction on N-ary Relational Data Based on Relatedness Evaluation](https://mlsanity.com/paper/2104.10424/) (2021)
- [Joint Language Semantic and Structure Embedding for Knowledge Graph Completion](https://mlsanity.com/paper/2209.08721/) (2022)
- [KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph Completion](https://mlsanity.com/paper/2208.07622/) (2022)
- [Sem@K: Is my knowledge graph embedding model semantic-aware?](https://mlsanity.com/paper/2301.05601/) (2023)
- [Geometric Relational Embeddings: A Survey](https://mlsanity.com/paper/2304.11949/) (2023)
- [Negative Sampling in Knowledge Graph Representation Learning: A Review](https://mlsanity.com/paper/2402.19195/) (2024)
- [A Survey of Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2203.02982/) (2022)
- [Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligence](https://mlsanity.com/paper/2301.04020/) (2022)
- [Conformalized Answer Set Prediction for Knowledge Graph Embedding](https://mlsanity.com/paper/2408.08248/) (2024)
- [PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph Completion](https://mlsanity.com/paper/2210.13715/) (2022)
- [Discovering Fine-Grained Semantics in Knowledge Graph Relations](https://mlsanity.com/paper/2202.08917/) (2022)
- [ConvD: Attention Enhanced Dynamic Convolutional Embeddings for Knowledge Graph Completion](https://mlsanity.com/paper/2312.07589/) (2023)
- [RelWalk A Latent Variable Model Approach to Knowledge Graph Embedding](https://mlsanity.com/paper/2101.10070/) (2021)
- [KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings](https://mlsanity.com/paper/2112.04871/) (2021)
- [Knowledge Graph Embedding by Normalizing Flows](https://mlsanity.com/paper/2409.19977/) (2024)
- [A Statistical Relational Approach to Learning Distance-based GCNs](https://mlsanity.com/paper/2102.07007/) (2021)
- [KARL-Trans-NER: Knowledge Aware Representation Learning for Named Entity Recognition using Transformers](https://mlsanity.com/paper/2111.15436/) (2021)
- [Farspredict: A benchmark dataset for link prediction](https://mlsanity.com/paper/2303.14647/) (2023)
- [SparseTransX: Efficient Training of Translation-Based Knowledge Graph Embeddings Using Sparse Matrix Operations](https://mlsanity.com/paper/2502.16949/) (2025)
- [Autoregressive Models for Knowledge Graph Generation](https://mlsanity.com/paper/2602.06707/) (2026)
- [EvoRAG: Making Knowledge Graph-based RAG Automatically Evolve through Feedback-driven Backpropagation](https://mlsanity.com/paper/2604.15676/) (2026)
- [Relations Are Channels: Knowledge Graph Embedding via Kraus Decompositions](https://mlsanity.com/paper/2605.10317/) (2026)
- [BLADE: Distilled LLM Regularization for Calibrated Knowledge Graph Completion](https://mlsanity.com/paper/2609.29487/) (2026)

## Abstract (arXiv)

Recently, knowledge graph embedding, which projects symbolic entities and relations into continuous vector space, has become a new, hot topic in artificial intelligence. This paper addresses a new issue of multiple relation semantics that a relation may have multiple meanings revealed by the entity pairs associated with the corresponding triples, and proposes a novel Gaussian mixture model for embedding, TransG. The new model can discover latent semantics for a relation and leverage a mixture of relation component vectors for embedding a fact triple. To the best of our knowledge, this is the first generative model for knowledge graph embedding, which is able to deal with multiple relation semantics. Extensive experiments show that the proposed model achieves substantial improvements against the state-of-the-art baselines.