TransG : A Generative Model for Knowledge Graph Embedding

Xiao, Han; Huang, Minlie; Zhu, Xiaoyan

doi:10.18653/v1/p16-1219

Cited by 318 publications

(161 citation statements)

References 14 publications

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“…Then some following embedding methods for heterogeneous networks are proposed, like Metapath2vec [6] , HNE [3], BiNE [7] and EOE [23]. Some works further focuses on knowledge graph embedding [22]. Although these network embedding methods are able to preserve the local structures of the vertices, most of them are not specifically designed for the task of recommendation.…”

Section: Network Representation Learningmentioning

confidence: 99%

RNE: A Scalable Network Embedding for Billion-Scale Recommendation

Lin

Wang

Guan

et al. 2019

Advances in Knowledge Discovery and Data Mining

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Nowadays designing a real recommendation system has been a critical problem for both academic and industry. However, due to the huge number of users and items, the diversity and dynamic property of the user interest, how to design a scalable recommendation system, which is able to efficiently produce effective and diverse recommendation results on billion-scale scenarios, is still a challenging and open problem for existing methods. In this paper, given the user-item interaction graph, we propose RNE, a data-efficient Recommendationbased Network Embedding method, to give personalized and diverse items to users. Specifically, we propose a diversity-and dynamics-aware neighbor sampling method for network embedding. On the one hand, the method is able to preserve the local structure between the users and items while modeling the diversity and dynamic property of the user interest to boost the recommendation quality. On the other hand the sampling method can reduce the complexity of the whole method theoretically to make it possible for billion-scale recommendation. We also implement the designed algorithm in a distributed way to further improves its scalability. Experimentally, we deploy RNE on a recommendation scenario of Taobao, the largest E-commerce platform in China, and train it on a billion-scale user-item graph. As is shown on several online metrics on A/B testing, RNE is able to achieve both high-quality and diverse results compared with CF-based methods. We also conduct the offline experiments on Pinterest dataset comparing with several state-of-the-art recommendation methods and network embedding methods. The results demonstrate that our method is able to produce a good result while runs much faster than the baseline methods.

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Section: Network Representation Learningmentioning

confidence: 99%

RNE: A Scalable Network Embedding for Billion-Scale Recommendation

Lin

Wang

Guan

et al. 2019

Advances in Knowledge Discovery and Data Mining

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“…TransH (Wang et al, 2014b) introduced the relation-specific hyperplane to translate the entities. Similar work utilizing only the structure of the graph include ManifoldE (Xiao et al, 2015b), TransG (Xiao et al, 2015a), TransD (Ji et al, 2015), TransM (Fan et al, 2014), HolE (Nickel et al, 2016b) and ProjE (Shi and Weninger, 2017).…”

Section: Knowledge Graph Representationsmentioning

confidence: 99%

Learning beyond Datasets: Knowledge Graph Augmented Neural Networks for Natural Language Processing

Annervaz¹,

Chowdhury

Dukkipati

2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: Hu

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Machine Learning has been the quintessential solution for many AI problems, but learning models are heavily dependent on specific training data. Some learning models can be incorporated with prior knowledge using a Bayesian setup, but these learning models do not have the ability to access any organized world knowledge on demand. In this work, we propose to enhance learning models with world knowledge in the form of Knowledge Graph (KG) fact triples for Natural Language Processing (NLP) tasks. Our aim is to develop a deep learning model that can extract relevant prior support facts from knowledge graphs depending on the task using attention mechanism. We introduce a convolutionbased model for learning representations of knowledge graph entity and relation clusters in order to reduce the attention space. We show that the proposed method is highly scalable to the amount of prior information that has to be processed and can be applied to any generic NLP task. Using this method we show significant improvement in performance for text classification with 20Newsgroups (News20) & DBPedia datasets, and natural language inference with Stanford Natural Language Inference (SNLI) dataset. We also demonstrate that a deep learning model can be trained with substantially less amount of labeled training data, when it has access to organized world knowledge in the form of a knowledge base.

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“…In TransH [35] and TransR [15] the translations are performed in the relations space, which is different from the entities space, and require projection matrices to map the entities onto the relations space. TransG [37] and CTransR [15] incorporate multiple relation semantics, where a relation may have multiple meanings determined by the entities pair associated with the relation. PTransE [14] extends TransE by considering relation paths as regular relations, which makes the number of relations considered grow exponentially.…”

Section: Related Workmentioning

confidence: 99%

Detection of Relation Assertion Errors in Knowledge Graphs

Melo

Paulheim

2017

Proceedings of the Knowledge Capture Conference

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Although the link prediction problem, where missing relation assertions are predicted, has been widely researched, error detection did not receive as much attention. In this paper, we investigate the problem of error detection in relation assertions of knowledge graphs, and we propose an error detection method which relies on path and type features used by a classifier for every relation in the graph exploiting local feature selection. We perform an extensive evaluation on a variety of datasets, backed by a manual evaluation on DBpedia and NELL, and we propose and evaluate heuristics for the selection of relevant graph paths to be used as features in our method.

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TransG : A Generative Model for Knowledge Graph Embedding

Cited by 318 publications

References 14 publications

RNE: A Scalable Network Embedding for Billion-Scale Recommendation

RNE: A Scalable Network Embedding for Billion-Scale Recommendation

Learning beyond Datasets: Knowledge Graph Augmented Neural Networks for Natural Language Processing

Detection of Relation Assertion Errors in Knowledge Graphs

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