Knowledge graph has gained increasing attention in recent years for its successful applications of numerous tasks. Despite the rapid growth of knowledge construction, knowledge graphs still suffer from severe incompletion and inevitably involve various kinds of errors. Several attempts have been made to complete knowledge graph as well as to detect noise. However, none of them considers unifying these two tasks even though they are inter-dependent and can mutually boost the performance of each other. In this paper, we proposed to jointly combine these two tasks with a unified Generative Adversarial Networks (GAN) framework to learn noise-aware knowledge graph embedding. Extensive experiments have demonstrated that our approach is superior to existing state-of-the-art algorithms both in regard to knowledge graph completion and error detection. Knowledge graph, as a well-structured effective representation of knowledge, plays a pivotal role in many real-world applications such as web search (Graupmann et al., 2005) , question answering (Hao et al., 2017; Yih et al., 2015) , and personalized recommendation (Zhang et al., 2016) . It is constructed by extracting information as the form of triple from unstructured text using information extraction systems. Each triple (h, r, t) represents a relation r between a head entity h and a tail entity t. Recent years have witnessed extensive construction of knowledge graph, such as Freebase (Bollacker et al., 2008) , DBPedia (Auer et al., 2007) , and YAGO (Suchanek et al., 2007) . However, these knowledge graphs suffer from severe sparsity as we can never collect all the information. Moreover, due to the huge volumes of web resources, the task to construct knowledge graph usually involves automatic mechanisms to avoid human supervision and thus inevitably introduces many kinds of errors, including ambiguous, conflicting and erroneous and redundant information. To address these shortcomings, various methods for knowledge graph refinement have been proposed, whose goals can be arguably classified into two categories: (1) knowledge graph completion, the task to add missing knowledge to the knowledge graph, and (2) error detection, the task to identify incorrect triples in the knowledge graph. Knowledge graph embedding (KGE) currently hold the state-of-the-art in knowledge graph completion for their promising results (Bordes et al., 2013; Yang et al., 2014) . Nonetheless, they highly rely on high quality training data and thus are lack of robustness to noise (Pujara et al., 2017) . Error detection in knowledge graph is a challenging problem due to the difficulty of obtaining noisy data. Reasoning based methods are the most widely used methods for this task (Paulheim, 2017) . Without the guidance of noisy data, they detect errors by performing reasoning over the knowledge graph to determine the correctness of a triple. A rich ontology information is required for such kind of methods and thus impede its application for real-world knowledge graphs. Existing works consider knowledge graph embedding and error detection independently whereas these two tasks are inter-dependent and can greatly influence each other. On one hand, error detection model is extremely useful to prepare reliable data for knowledge graph embedding. On the other hand, high quality embedding learned by KGE model provides a basis for reasoning to identify noisy data. Inspired by the recent advances of generative adversarial deep models (Goodfellow et al., 2014) , in this paper, we proposed to jointly combine these two tasks with a unified GAN framework, known as NoiGAN, to learn noise-aware knowledge graph embedding. In general, NoiGAN consists of two main components, a noise-aware KGE model to learn robuster representation of knowledge and an adversarial learning framework for error detection. During the training, noiseaware KGE model takes the confidence score learned by GAN as guidance to eliminate the noisy data from the learning process whereas the GAN requires that KGE model continuously provides high quality embedding as well as credible positive examples to model the discriminator and the generator. Cooperation between the two components drives both to improve their capability. The main contributions of this paper are summarized as follows: • We propose a unified generative adversarial framework NoiGAN, to learn noise-aware knowledge graph embedding. Under the framework, the KGE model and error detection model could benefit from each other: the error detection model prepares reliable data for KGE model to improve the quality of embedding it learns, while the KGE model provides a promising reasoning model for the error detection model to better distinguish noisy triples from the correct one. • Our proposed framework can be easily generalized to various KGE models to enhance their ability in dealing with noisy knowledge graph. • We experimentally demonstrate that our new algorithm is superior to existing state-of-the-art algorithms. The KGE model and GAN can alternately and iteratively boost performance in terms of both knowledge graph completion and noise detection. In this paper, we propose a novel framework NoiGAN, to jointly combine the tasks of knowledge graph completion and error detection for noise aware knowledge graph embedding learning. It consists of two main components, a noise-aware KGE model for knowledge graph completion and an adversarial learning framework for error detection. Under the framework, the noise-aware KGE model and the adversarial learning framework can alternately and iteratively boost performance of each other. Extensive experiments show the superiority of our proposed NoiGAN both in regard to knowledge graph completion and error detection.
We proposed a unified Generative Adversarial Networks (GAN) framework to learn noise-aware knowledge graph embedding.
{"src": "scitldr-AIC_train", "paper_id": "rkgTdkrtPH"}