Data Augmentation for Few-Shot Knowledge Graph Completion from Hierarchical Perspective (2022)

Abstract: Few-shot knowledge graph completion (FKGC) has become a new research focus in the field of knowledge graphs in recent years, which aims to predict the missing links for relations that only have a few associative triples. Existing models attempt to solve the problem via learning entity and relation representations. However, the limited training data severely hinders the performance of existing models. To this end, we propose to solve the FKGC problem with the data augmentation technique. Specifically, we perform data augmentation from two perspectives, i.e., inter-task view and intra-task view. The former generates new tasks for FKGC, while the latter enriches the support or query set for an individual task. It is worth noting that the proposed framework can be applied to a number of existing FKGC models. Experimental evaluation on two public datasets indicates our model is capable of achieving substantial improvements over baselines.

## introduction knowledge graphs (kgs) are structured semantic knowledge bases used to describe concepts and their interrelationships in the physical world in symbolic form. many kgs in the real world, such as freebase @xcite , yago @xcite , wordnet @xcite , wikidata @xcite and nell @xcite , consist of triple facts in the form of (head entity, relation, tail entity), e.g., (paris, capitalof, france) indicates that paris is the capital of france. kgs have been introduced into various downstream tasks of nlp, such as question answering @xcite , dialogue systems @xcite and information extraction @xcite , etc. the integrity of kg promotes the performance of downstream tasks. however, kgs in the real world are far from complete and comprehensive. therefore, it is necessary to complete kgs by inferring new triple facts. to complete kgs, most existing embeddingbased kg completion models require adequate triples for each relation as training data, such as transe @xcite , rotate @xcite and conve @xcite . however, in reality, the number of triples for each relation conforms to a long-tail distribution @xcite , i.e., only a small number of relations occur frequently, while most relations only occur a few times in a kg. this phenomenon hinders to learning reliable representations for infrequent relations and further degrades the kg completion performance. this has motivated an emerging research topic named few-shot knowledge graph completion (fkgc), where one task is to predict the tail entity t in a query (h, r, ?) given only a few entity pairs of the task relation r. gmatching @xcite is the first study on the fkgc task, which proposes the basic framework and problem formulation. fsrl @xcite and faan @xcite further improve the attention mechanism of the gmatching framework. metar @xcite and gana @xcite adopt the meta-based paradigm in meta-learning as the basic architecture. although the above methods achieve promising results for the fkgc problem, they still suffer from the limited training data for each relation. to this end, we propose to alleviate the above issue using the data augmentation technique. specifically, as shown in figure 1 , we aim to augment the data of each task within its own distribution, and densify the task distribution by providing interpolated tasks. therefore, we propose to augment data from a hierarchical perspective. the inter-task view generates new tasks for the fkgc model. and the intra-task view provides entity pairs for each individual task. this setting is capable of enriching luxuriant data and densifying the data distribution for fkgc models, which is beneficial to achieving better performances. we propose two data augmentation methods for each view to enhance the existing fkgc model. particularly, the proposed technique is general and can be applied to a number of existing fkgc models. to the best of our knowledge, this is the first work to solve the fkgc task using the data augmentation technique. finally, experimental results validate the effectiveness of the proposed method. ## background in this section, we provide problem formulation and the settings of fkgc. ## methodology the section describes the details of the data augmentation for fkgc. it falls into two data augmentation methods from the task perspective: intratask augmentation and inter-task augmentation. inter-task augmentation generates new tasks for the fkgc model, and the intra-task augmentation provides entity pairs for each individual task. we will describe how each of these data augmentation methods is applied to metric learning-based methods and meta learner-based methods. ## conclusion to alleviate the limited data problem in the fkgc task. in this paper, we propose to utilize the data augmentation technique to enrich the training set for fkgc models. specifically, we design the data augmentation method from hierarchical perspectives. the inter-task perspective generates new tasks for the fkgc task, while the intra-task perspective provides more entity pairs for each task. furthermore, in order to fully perform data augmentation, we design two augmentation methods for each perspective, i.e., inverse augmentation and interpolation augmentation for the inter-task view, query augmentation and support augmentation for the intra-task view. experimental results validate the effectiveness of the proposed method.