Due to the semantic complexity of the Relation extraction (RE) task, obtaining high-quality human labelled data is an expensive and noisy process.To improve the sample efficiency of the models, semi-supervised learning (SSL) methods aim to leverage unlabelled data in addition to learning from limited labelled data points.Recently, strong data augmentation combined with consistency-based semi-supervised learning methods have advanced the state of the art in several SSL tasks.However, adapting these methods to the RE task has been challenging due to the difficulty of data augmentation for RE.In this work, we leverage the recent advances in controlled text generation to perform high quality data augmentation for the RE task.We further introduce small but significant changes to model architecture that allows for generation of more training data by interpolating different data points in their latent space.These data augmentations along with consistency training result in very competitive results for semi-supervised relation extraction on four benchmark datasets.

Real-world knowledge graphs are often characterized by low-frequency relations-a challenge that has prompted an increasing interest in few-shot link prediction methods.These methods perform link prediction for a set of new relations, unseen during training, given only a few example facts of each relation at test time.In this work, we perform a systematic study on a spectrum of models derived by generalizing the current state of the art for few-shot link prediction, with the goal of probing the limits of learning in this fewshot setting.We find that a simple zero-shot baseline-which ignores any relation-specific information-achieves surprisingly strong performance.Moreover, experiments on carefully crafted synthetic datasets show that having only a few examples of a relation fundamentally limits models from using fine-grained structural information and only allows for exploiting the coarse-grained positional information of entities.Together, our findings challenge the implicit assumptions and inductive biases of prior work and highlight new directions for research in this area.

The dominant paradigm for relation prediction in knowledge graphs involves learning and operating on latent representations (i.e., embeddings) of entities and relations. However, these embedding-based methods do not explicitly capture the compositional logical rules underlying the knowledge graph, and they are limited to the transductive setting, where the full set of entities must be known during training. Here, we propose a graph neural network based relation prediction framework, GraIL, that reasons over local subgraph structures and has a strong inductive bias to learn entity-independent relational semantics. Unlike embedding-based models, GraIL is naturally inductive and can generalize to unseen entities and graphs after training. We provide theoretical proof and strong empirical evidence that GraIL can rep-resent a useful subset of first-order logic and show that GraIL outperforms existing rule-induction baselines in the inductive setting. We also demonstrate significant gains obtained by ensembling GraIL with various knowledge graph embedding methods in the transductive setting, highlighting the complementary inductive bias of our method.