Parasitic Network: Zero-Shot Relation Extraction for Knowledge Graph Populating

The relation tuple is the basic unit of the knowledge graph. Conventional relation extraction methods can only identify limited relation classes and not recognize the unseen relation types that have no pre-labeled training data. In this paper, we explore the zero-shot relation extraction to overcome the challenge. The only requisite information about an unseen type is the label name. We propose a Parasitic Neural Network (PNN), where unseen types are parasitic on seen types to get automatic annotation and training. The model learns a mapping between the feature representations of text samples and the distributions of unseen types in a shared semantic space. Experiment results show that our model significantly outperforms others on the unseen relation extraction task and achieves effect improvement of more than 20% when there are not any manual annotations or additional resources. This model, with good performance and fast implementation, can support the industrial knowledge graph populating.