# Edge: Enriching Knowledge Graph Embeddings with External Text

arXiv: https://arxiv.org/abs/2104.04909 · HTML: https://mlsanity.com/paper/2104.04909/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 29 references that are in the map.
- First posted on arXiv 11 Apr 2021.

## Builds on (most cited first, 17 of 17)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space](https://mlsanity.com/paper/1902.10197/) (2019)
- [MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing](https://mlsanity.com/paper/1905.00067/) (2019)
- [Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text](https://mlsanity.com/paper/1809.00782/) (2018)
- [Adversarially Regularized Graph Autoencoder for Graph Embedding](https://mlsanity.com/paper/1802.04407/) (2018)
- [Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning](https://mlsanity.com/paper/1903.08948/) (2019)
- [Commonsense Knowledge Base Completion with Structural and Semantic Context](https://mlsanity.com/paper/1910.02915/) (2019)
- [Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning](https://mlsanity.com/paper/2009.06946/) (2020)
- [Co-embedding of Nodes and Edges with Graph Neural Networks](https://mlsanity.com/paper/2010.13242/) (2020)
- [CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation](https://mlsanity.com/paper/2010.15620/) (2020)
- [Learning Contextualized Knowledge Structures for Commonsense Reasoning](https://mlsanity.com/paper/2010.12873/) (2020)
- [Mapping Text to Knowledge Graph Entities using Multi-Sense LSTMs](https://mlsanity.com/paper/1808.07724/) (2018)
- [Learning to Make Predictions on Graphs with Autoencoders](https://mlsanity.com/paper/1802.08352/) (2018)

## Built on by (most cited first, 13 of 13)

- [Knowledge Distillation on Graphs: A Survey](https://mlsanity.com/paper/2302.00219/) (2023)
- [Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recommendation](https://mlsanity.com/paper/2212.11868/) (2022)
- [Exploring New Frontiers in Agricultural NLP: Investigating the Potential of Large Language Models for Food Applications](https://mlsanity.com/paper/2306.11892/) (2023)
- [State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding](https://mlsanity.com/paper/2309.12482/) (2023)
- [AGI for Agriculture](https://mlsanity.com/paper/2304.06136/) (2023)
- [Automated Graph Learning via Population Based Self-Tuning GCN](https://mlsanity.com/paper/2107.04713/) (2021)
- [Knowledge-Enhanced Conversational Recommendation via Transformer-based Sequential Modelling](https://mlsanity.com/paper/2412.02415/) (2024)
- [Knowledge Graph Induction enabling Recommending and Trend Analysis: A Corporate Research Community Use Case](https://mlsanity.com/paper/2207.05188/) (2022)
- [Benchmarking and Analyzing In-context Learning, Fine-tuning and Supervised Learning for Biomedical Knowledge Curation: a focused study on chemical entities of biological interest](https://mlsanity.com/paper/2312.12989/) (2023)
- [An LLM-Guided Query-Aware Inference System for GNN Models on Large Knowledge Graphs](https://mlsanity.com/paper/2603.04545/) (2026)
- [Learning to Borrow -- Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion](https://mlsanity.com/paper/2204.13097/) (2022)
- [Constraint Multi-class Positive and Unlabeled Learning for Distantly Supervised Named Entity Recognition](https://mlsanity.com/paper/2504.04963/) (2025)
- [Are a Thousand Words Better Than a Single Picture? Beyond Images -- A Framework for Multi-Modal Knowledge Graph Dataset Enrichment](https://mlsanity.com/paper/2603.16974/) (2026)

## Abstract (arXiv)

Knowledge graphs suffer from sparsity which degrades the quality of representations generated by various methods. While there is an abundance of textual information throughout the web and many existing knowledge bases, aligning information across these diverse data sources remains a challenge in the literature. Previous work has partially addressed this issue by enriching knowledge graph entities based on "hard" co-occurrence of words present in the entities of the knowledge graphs and external text, while we achieve "soft" augmentation by proposing a knowledge graph enrichment and embedding framework named Edge. Given an original knowledge graph, we first generate a rich but noisy augmented graph using external texts in semantic and structural level. To distill the relevant knowledge and suppress the introduced noise, we design a graph alignment term in a shared embedding space between the original graph and augmented graph. To enhance the embedding learning on the augmented graph, we further regularize the locality relationship of target entity based on negative sampling. Experimental results on four benchmark datasets demonstrate the robustness and effectiveness of Edge in link prediction and node classification.