# Zero-Shot Learning with Common Sense Knowledge Graphs

arXiv: https://arxiv.org/abs/2006.10713 · HTML: https://mlsanity.com/paper/2006.10713/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2020.

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

- [Does CLIP Bind Concepts? Probing Compositionality in Large Image Models](https://mlsanity.com/paper/2212.10537/) (2022)
- [Debiased Learning from Naturally Imbalanced Pseudo-Labels](https://mlsanity.com/paper/2201.01490/) (2022)
- [Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey](https://mlsanity.com/paper/2402.05391/) (2024)
- [Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2112.10006/) (2021)
- [Exploring Hierarchical Graph Representation for Large-Scale Zero-Shot Image Classification](https://mlsanity.com/paper/2203.01386/) (2022)
- [A survey on knowledge-enhanced multimodal learning](https://mlsanity.com/paper/2211.12328/) (2022)
- [What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data](https://mlsanity.com/paper/2106.06410/) (2021)
- [Benchmarking Knowledge-driven Zero-shot Learning](https://mlsanity.com/paper/2106.15047/) (2021)
- [Disentangled Action Recognition with Knowledge Bases](https://mlsanity.com/paper/2207.01708/) (2022)
- [Deep Semantic-Visual Alignment for Zero-Shot Remote Sensing Image Scene Classification](https://mlsanity.com/paper/2402.02094/) (2024)
- [Hierarchical Material Recognition from Local Appearance](https://mlsanity.com/paper/2505.22911/) (2025)
- [Understanding Substructures in Commonsense Relations in ConceptNet](https://mlsanity.com/paper/2210.01263/) (2022)
- [TAGLETS: A System for Automatic Semi-Supervised Learning with Auxiliary Data](https://mlsanity.com/paper/2111.04798/) (2021)
- [Recognizing Unseen States of Unknown Objects by Leveraging Knowledge Graphs](https://mlsanity.com/paper/2307.12179/) (2023)
- [Knowledge Graph Enhanced Generative Multi-modal Models for Class-Incremental Learning](https://mlsanity.com/paper/2503.18403/) (2025)
- [Tight Lower Bounds on Worst-Case Guarantees for Zero-Shot Learning with Attributes](https://mlsanity.com/paper/2205.13068/) (2022)
- [Knowledge Graph-Based Explainable and Generalized Zero-Shot Semantic Communications](https://mlsanity.com/paper/2507.02291/) (2025)

## Abstract (arXiv)

Zero-shot learning relies on semantic class representations such as hand-engineered attributes or learned embeddings to predict classes without any labeled examples. We propose to learn class representations by embedding nodes from common sense knowledge graphs in a vector space. Common sense knowledge graphs are an untapped source of explicit high-level knowledge that requires little human effort to apply to a range of tasks. To capture the knowledge in the graph, we introduce ZSL-KG, a general-purpose framework with a novel transformer graph convolutional network (TrGCN) for generating class representations. Our proposed TrGCN architecture computes non-linear combinations of node neighbourhoods. Our results show that ZSL-KG improves over existing WordNet-based methods on five out of six zero-shot benchmark datasets in language and vision.