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

TL;DR

This paper introduces a logic-guided semantic learning model for zero-shot relation classification, leveraging semantic knowledge and logic rules to recognize unseen relations, outperforming previous approaches.

Contribution

The paper proposes a novel approach combining knowledge graph embeddings and logic rules to improve zero-shot relation classification, addressing limitations of prior methods.

Findings

The method effectively generalizes to unseen relation types.

It achieves significant improvements over existing approaches.

The approach demonstrates robustness across various datasets.

Abstract

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.