# Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification

arXiv: https://arxiv.org/abs/2010.16068 · HTML: https://mlsanity.com/paper/2010.16068/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2020.

## Built on by (most cited first, 18 of 18)

- [KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction](https://mlsanity.com/paper/2104.07650/) (2021)
- [Generative Knowledge Graph Construction: A Review](https://mlsanity.com/paper/2210.12714/) (2022)
- [OntoZSL: Ontology-enhanced Zero-shot Learning](https://mlsanity.com/paper/2102.07339/) (2021)
- [Knowledge-aware Zero-Shot Learning: Survey and Perspective](https://mlsanity.com/paper/2103.00070/) (2021)
- [Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2112.10006/) (2021)
- [Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt Tuning](https://mlsanity.com/paper/2205.02355/) (2022)
- [Information Extraction in Low-Resource Scenarios: Survey and Perspective](https://mlsanity.com/paper/2202.08063/) (2022)
- [Prompt-based Zero-shot Relation Extraction with Semantic Knowledge Augmentation](https://mlsanity.com/paper/2112.04539/) (2021)
- [Disentangled Ontology Embedding for Zero-shot Learning](https://mlsanity.com/paper/2206.03739/) (2022)
- [Benchmarking Knowledge-driven Zero-shot Learning](https://mlsanity.com/paper/2106.15047/) (2021)
- [Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study](https://mlsanity.com/paper/2210.10678/) (2022)
- [Geometry-Aware Adaptation for Pretrained Models](https://mlsanity.com/paper/2307.12226/) (2023)
- [Normal vs. Adversarial: Salience-based Analysis of Adversarial Samples for Relation Extraction](https://mlsanity.com/paper/2104.00312/) (2021)
- [Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning](https://mlsanity.com/paper/2403.07078/) (2024)
- [Multilingual Entity and Relation Extraction from Unified to Language-specific Training](https://mlsanity.com/paper/2301.04434/) (2023)
- [IMKGA-SM: Interpretable Multimodal Knowledge Graph Answer Prediction via Sequence Modeling](https://mlsanity.com/paper/2301.02445/) (2023)
- [Multimodal Representation Learning Conditioned on Semantic Relations](https://mlsanity.com/paper/2508.17497/) (2025)
- [A Few-Shot Learning Focused Survey on Recent Named Entity Recognition and Relation Classification Methods](https://mlsanity.com/paper/2310.19055/) (2023)

## Abstract (arXiv)

Relation classification aims to extract semantic relations between entity pairs from the sentences. However, most existing methods can only identify seen relation classes that occurred during training. To recognize unseen relations at test time, we explore the problem of zero-shot relation classification. Previous work regards the problem as reading comprehension or textual entailment, which have to rely on artificial descriptive information to improve the understandability of relation types. Thus, rich semantic knowledge of the relation labels is ignored. In this paper, we propose a novel logic-guided semantic representation learning model for zero-shot relation classification. Our approach builds connections between seen and unseen relations via implicit and explicit semantic representations with knowledge graph embeddings and logic rules. Extensive experimental results demonstrate that our method can generalize to unseen relation types and achieve promising improvements.