NoiGAN: NOISE AWARE KNOWLEDGE GRAPH EMBEDDING WITH GAN

1 INTRODUCTION . 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 . 2 RELATED WORK . 2.1 KNOWLEDGE GRAPH COMPLETION . Embedding based methods currently hold the state-of-the-art in knowledge graph completion for their promising results ( Wang et al. , 2017b ) . They aim to capture the similarity of entities by embedding entities and relations into continuous low-dimensional vectors . Existing methods can be roughly divided into two categories : translational distance models and semantic matching models . Translational distance models measure the plausibility of a fact as the distance between two entities after a translation carried out by the relation . TransE ( Bordes et al. , 2013 ) , TransH ( Wang et al. , 2014 ) and TransR ( Lin et al. , 2015 ) are the representative approaches in this category . Semantic matching models measure plausibility of facts by matching latent semantics of entities and relations embodied in their vector space representations . The typical models include RESCAL ( Nickel et al. , 2011 ) , DistMult ( Yang et al. , 2014 ) and ComplEx ( Trouillon et al. , 2016 ) . To optimize the KGE model , negative sampling is usually required to minimize the margin based ranking loss . A conventional method to construct negative samples is randomly sampling . However , negative samples generated through a random mode are often too easy to be discriminated from positive facts and thus make little contribute towards the training . Some recent works proposed to incorporate GAN for better negative sampling to improve the quality of embeddings ( Wang et al. , 2018 ; Cai & Wang , 2017 ) . Nonetheless , none of the above methods has taken the potential noisy data into consideration , which leads to their sensitivity to unreliable data ( Pujara et al. , 2017 ) . In this paper , we proposed a novel technique to enable current embedding models to cope with noisy data . 2.2 ERROR DETECTION IN KNOWLEDGE GRAPH . Due to lack of noisy data samples , error detection in knowledge graph is a challenging task . Existing methods can be either ontology based or anomaly detection based . Ontology based methods address this problem by exploring additional ontology information . A larger number of ontology reasoners are developed to utilize logic programming to derive uncovering contradictions among observed facts ( Luther et al. , 2009 ; Dentler et al. , 2011 ; Ding et al. , 2007 ) . Rich ontology information is required for such kind of methods and thus impede its application to real-world knowledge graphs . Another kind of methods is anomaly detection based methods ( Wienand & Paulheim , 2014 ; Fleischhacker et al. , 2014 ) . The main drawback of anomaly detection based methods are that they do not necessarily identify errors , but also natural outliers , which will compromise the objectivity of its results . More recently , a novel confidence-aware framework ( Xie et al. , 2018 ; Shan et al. , 2018 ) proposed to incorporate triple confidence into KGE model to detect noises while learning knowledge representations simultaneously . However , it measures the triple confidence merely based on the how well the triple fits the model , which is easily affected by model bias . 3 PROPOSED METHOD : NOIGAN . In this section , we illustrate our proposed framework , NoiGAN , in details . The goal of our proposed framework is to learn noise-aware KG embedding with Generative Adversarial Networks ( GAN ) . As shown in Figure 1 , it consists of three components : ( i ) Noise aware KGE model , which incorporates a confidence scoreC ( h , r , t ) into the KGE model to isolate the impact of noise over embedding vectors . Meanwhile , it provides high quality embedding to model discriminator and generator ( Section 3.1 ) ; ( ii ) Triple generator G ( ( h′ , r , t′ ) | ( h , r , t ) ; θG ) , which learns to generate the most likely triples to be the noisy triples by corrupting the correct triple ( h , r , t ) . It aims at providing reliable noisy data for the discriminator as well as noise aware KGE model ( Section 3.2.1 ) ; ( iii ) Discriminator D ( ( h , r , t ) ; θD ) , which tries to distinguish correct triples from noisy triples , and assigns a confidence score for each triple fact to describe the correctness of it . The discriminator is used to determine confidence score C ( h , r , t ) in the noise aware KGE model . It also naturally provides guidance to the generator to produce higher quality noisy data ( Section 3.2.2 ) . Before further discussing the detail of the algorithm , we first give a formal definition to knowledge graph . A knowledge graph , denoted by G = { E , R , T } , consists of a set of entities E , a set of relations R and a set of of observed facts T = { ( h , r , t ) | h , t ∈ E , r ∈ R } . A fact is represented by a triple ( h , r , t ) , where h , t and r denote head entity , tail entity , and relation , respectively . 3.1 NOISE AWARE KNOWLEDGE GRAPH EMBEDDING MODEL . To learn a more robust representation for noisy knowledge graph , we propose a noise-aware KGE model to eliminate the impact of noise over embedding vectors by incorporating confidence score into KGE model . It can be easily adapted to any KGE models . For example , we can follow TransE to represent entities and relations . Given a fact ( h , r , t ) , TransE aims to learn corresponding low dimensional vectors h , r and t in the same space for h , r and t , so that the distance between h + r and t is minimized . The scoring function of TransE is then defined as fr ( h , t ) = ‖h+ r− t‖21 . To optimize the KGE model , we use a loss function similar to the negative sampling loss in ( Mikolov et al. , 2013 ) according to ( Sun et al. , 2019 ) . ∑ ( h , r , t ) ∈T [ ( − log σ ( γ − fr ( h , t ) ) + ∑ ( h′ , r , t′ ) ∈N ( h , r , t ) 1 |N ( h , r , t ) | log σ ( γ − fr ( h′ , t′ ) ) ) ] where γ is the margin , σ is the sigmoid function , T represents the observed facts from the knowledge graph and ( h′ , r , t′ ) is the negative sample for triple ( h , r , t ) . To reduce the effects of randomness , we sample multiple negative triples for each observed fact . We denote the negative triples set of a triple ( h , r , t ) as N ( h , r , t ) . Negative triples set is constructed by replacing the head or tail entity of the observed triples with an entity sampled randomly from entity set E : N ( h , r , t ) = { ( h′ , r , t ) |h′ ∈ E } ∪ { ( h , r , t′ ) |t′ ∈ E } , ( h , r , t ) ∈ T A vast majority of existing embedding models assume that all triple facts hold true to knowledge graph , which is inappropriate . In fact , knowledge graph contains many kinds of errors ( e.g. , ambiguous , conflicting , erroneous , redundant ) due to the automatic construction process . To isolate the impact of noise over embedding vectors , following ( Shan et al. , 2018 ) , we adopted the concept confidence to describe whether a triple fact is noisy or not . In ( Shan et al. , 2018 ) , confidence of a triple is measured by how well the triple fits to the KGE model . A central issue with such measurement is that it is easily affected by model bias and thus lead to unreliability . Unlike ( Shan et al. , 2018 ) , we learned the confidence scores using a discriminator , which can be more impartial and promising . We will discuss how to obtain such discriminator in Section 3.2.2 . With the introduction of confidence , we can eliminate the noisy data from the learning process of KGE model . LKGE = ∑ ( h , r , t ) ∈T C ( h , r , t ) . [ ( − log σ ( γ−fr ( h , t ) ) + ∑ ( h′ , r , t′ ) ∈N ( h , r , t ) 1 |N ( h , r , t ) | log σ ( γ−fr ( h′ , t′ ) ) ) ] where C ( h , r , t ) is the confidence score assigned to each positive triple ( h , r , t ) . To be specific , C ( h , r , t ) can be both a binary variable or a soft value from the interval [ 0 , 1 ] . We denote the previous one as the hard version and the later one as the soft version .

Summary: This paper presented a jointly learning framework based on GAN for tackling both knowledge graph completion and noise detection simultaneously. Existing works only deal with each of task independently and did not investigate the benefits of coping with both tasks together. The paper is well motivated. In order to achieve them, the paper presented a GAN framework in order to train a noising KG embedding as well the generator and discriminator. The key connections between two parts are through the confidence of a noise triple and generation of the negative sample triples. The whole framework looks quite interesting and promising. The experimental results are provided to validate the effectiveness of the proposed model.