# A Survey of Knowledge Graph Embedding and Their Applications

arXiv: https://arxiv.org/abs/2107.07842 · HTML: https://mlsanity.com/paper/2107.07842/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 48 references that are in the map.
- First posted on arXiv 16 Jul 2021.

## Builds on (most cited first, 16 of 16)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space](https://mlsanity.com/paper/1902.10197/) (2019)
- [Convolutional 2D Knowledge Graph Embeddings](https://mlsanity.com/paper/1707.01476/) (2017)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [A Review of Relational Machine Learning for Knowledge Graphs](https://mlsanity.com/paper/1503.00759/) (2015)
- [Holographic Embeddings of Knowledge Graphs](https://mlsanity.com/paper/1510.04935/) (2015)
- [A Survey on Knowledge Graph-Based Recommender Systems](https://mlsanity.com/paper/2003.00911/) (2020)
- [Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction](https://mlsanity.com/paper/1911.09419/) (2019)
- [Knowledge Graph Embedding for Link Prediction: A Comparative Analysis](https://mlsanity.com/paper/2002.00819/) (2020)
- [Analogical Inference for Multi-Relational Embeddings](https://mlsanity.com/paper/1705.02426/) (2017)
- [A Semantic Matching Energy Function for Learning with Multi-relational Data](https://mlsanity.com/paper/1301.3485/) (2013)
- [Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval](https://mlsanity.com/paper/1805.07591/) (2018)
- [Research Commentary on Recommendations with Side Information: A Survey and Research Directions](https://mlsanity.com/paper/1909.12807/) (2019)
- [Knowledge Graph Embeddings and Explainable AI](https://mlsanity.com/paper/2004.14843/) (2020)

## Built on by (most cited first, 17 of 17)

- [Sequence-to-Sequence Knowledge Graph Completion and Question Answering](https://mlsanity.com/paper/2203.10321/) (2022)
- [RHO (ρ): Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding](https://mlsanity.com/paper/2212.01588/) (2022)
- [RAG-based Explainable Prediction of Road Users Behaviors for Automated Driving using Knowledge Graphs and Large Language Models](https://mlsanity.com/paper/2405.00449/) (2024)
- [Recommender systems based on graph embedding techniques: A comprehensive review](https://mlsanity.com/paper/2109.09587/) (2021)
- [pyRDF2Vec: A Python Implementation and Extension of RDF2Vec](https://mlsanity.com/paper/2205.02283/) (2022)
- [Navigable Proximity Graph-Driven Native Hybrid Queries with Structured and Unstructured Constraints](https://mlsanity.com/paper/2203.13601/) (2022)
- [Knowledge Graph Embedding Methods for Entity Alignment: An Experimental Review](https://mlsanity.com/paper/2203.09280/) (2022)
- [CleanGraph: Human-in-the-loop Knowledge Graph Refinement and Completion](https://mlsanity.com/paper/2405.03932/) (2024)
- [Personalized Federated Knowledge Graph Embedding with Client-Wise Relation Graph](https://mlsanity.com/paper/2406.11943/) (2024)
- [Dual Embodied-Symbolic Concept Representations for Deep Learning](https://mlsanity.com/paper/2203.00600/) (2022)
- [Integrating Knowledge Graph embedding and pretrained Language Models in Hypercomplex Spaces](https://mlsanity.com/paper/2208.02743/) (2022)
- [Accelerating Dynamic Network Embedding with Billions of Parameter Updates to Milliseconds](https://mlsanity.com/paper/2306.08967/) (2023)
- [An Ad-hoc graph node vector embedding algorithm for general knowledge graphs using Kinetica-Graph](https://mlsanity.com/paper/2407.15906/) (2024)
- [The Structure of Relation Decoding Linear Operators in Large Language Models](https://mlsanity.com/paper/2510.26543/) (2025)
- [Knowledgebra: An Algebraic Learning Framework for Knowledge Graph](https://mlsanity.com/paper/2204.07328/) (2022)
- [Node-based Knowledge Graph Contrastive Learning for Medical Relationship Prediction](https://mlsanity.com/paper/2310.10138/) (2023)
- [Sampling Foundational Transformer: A Theoretical Perspective](https://mlsanity.com/paper/2408.05822/) (2024)

## Abstract (arXiv)

Knowledge Graph embedding provides a versatile technique for representing knowledge. These techniques can be used in a variety of applications such as completion of knowledge graph to predict missing information, recommender systems, question answering, query expansion, etc. The information embedded in Knowledge graph though being structured is challenging to consume in a real-world application. Knowledge graph embedding enables the real-world application to consume information to improve performance. Knowledge graph embedding is an active research area. Most of the embedding methods focus on structure-based information. Recent research has extended the boundary to include text-based information and image-based information in entity embedding. Efforts have been made to enhance the representation with context information. This paper introduces growth in the field of KG embedding from simple translation-based models to enrichment-based models. This paper includes the utility of the Knowledge graph in real-world applications.