  In relation triplet extraction (RTE), recognizing unseen relations for which
there are no training instances is a challenging task. Efforts have been made
to recognize unseen relations based on question-answering models or relation
descriptions. However, these approaches miss the semantic information about
connections between seen and unseen relations. In this paper, We propose a
prompt-based model with semantic knowledge augmentation (ZS-SKA) to recognize
unseen relations under the zero-shot setting. We present a new word-level
analogy-based sentence translation rule and generate augmented instances with
unseen relations from instances with seen relations using that new rule. We
design prompts with weighted virtual label construction based on an external
knowledge graph to integrate semantic knowledge information learned from seen
relations. Instead of using the actual label sets in the prompt template, we
construct weighted virtual label words. We learn the representations of both
seen and unseen relations with augmented instances and prompts. We then
calculate the distance between the generated representations using prototypical
networks to predict unseen relations. Extensive experiments conducted on three
public datasets FewRel, Wiki-ZSL, and NYT, show that ZS-SKA outperforms other
methods under zero-shot setting. Results also demonstrate the effectiveness and
robustness of ZS-SKA.
