Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification
This addresses the problem of recognizing unseen relations in text for natural language processing applications, offering a novel approach beyond existing methods.
The paper tackles zero-shot relation classification by proposing a logic-guided semantic representation learning model that connects seen and unseen relations using knowledge graph embeddings and logic rules, achieving promising improvements on unseen relation types.
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.