  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.
