Semantically Smooth Knowledge Graph Embedding

Guo, Shu; Wang, Quan; Wang, Bin; Wang, Lihong; Guo, Li

doi:10.3115/v1/p15-1009

Cited by 126 publications

(71 citation statements)

References 23 publications

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“…Triplet classification [14], [15], [38], [51], [52], [53], [61], [142] is a specific application for knowledge graph. It aims to classify whether an unseen triplet < h, r, t > is correct or not, i.e., whether the relation between h and t is r.…”

Section: Triple Classificationmentioning

confidence: 99%

A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications

Cai

Zheng

Chang

2018

IEEE Trans. Knowl. Data Eng.

1,716

885

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Graph is an important data representation which appears in a wide diversity of real-world scenarios. Effective graph analytics provides users a deeper understanding of what is behind the data, and thus can benefit a lot of useful applications such as node classification, node recommendation, link prediction, etc. However, most graph analytics methods suffer the high computation and space cost. Graph embedding is an effective yet efficient way to solve the graph analytics problem. It converts the graph data into a low dimensional space in which the graph structural information and graph properties are maximumly preserved. In this survey, we conduct a comprehensive review of the literature in graph embedding. We first introduce the formal definition of graph embedding as well as the related concepts. After that, we propose two taxonomies of graph embedding which correspond to what challenges exist in different graph embedding problem settings and how the existing work address these challenges in their solutions. Finally, we summarize the applications that graph embedding enables and suggest four promising future research directions in terms of computation efficiency, problem settings, techniques and application scenarios.

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Section: Triple Classificationmentioning

confidence: 99%

A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications

Cai

Zheng

Chang

2018

IEEE Trans. Knowl. Data Eng.

1,716

885

View full text Add to dashboard Cite

show abstract

“…Many models have been proposed for KB embedding (Nickel et al, 2011;Bordes et al, 2013;Socher et al, 2013 2013; Wang et al, 2014a;Zhao et al, 2015), entity type and relationship domain (Guo et al, 2015;Chang et al, 2014), and relation path (Lin et al, 2015a;Gu et al, 2015). However, these methods solely rely on triple facts but neglect temporal order constraints between facts.…”

Section: Related Workmentioning

confidence: 99%

Encoding Temporal Information for Time-Aware Link Prediction

Jiang¹,

Li²,

Ge³

et al. 2016

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

111

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Most existing knowledge base (KB) embedding methods solely learn from time-unknown fact triples but neglect the temporal information in the knowledge base. In this paper, we propose a novel time-aware KB embedding approach taking advantage of the happening time of facts. Specifically, we use temporal order constraints to model transformation between time-sensitive relations and enforce the embeddings to be temporally consistent and more accurate. We empirically evaluate our approach in two tasks of link prediction and triple classification. Experimental results show that our method outperforms other baselines on the two tasks consistently.

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“…Besides triples in KGs, some embedding methods also utilize other information during learning, for examples, entity descriptions in [40] [43][39] [42], entity types in [12] [45], entity images in [44], and paths in [11][21] [25], which can be regarded as a kind of rules.…”

Section: Embedding Learningmentioning

confidence: 99%

Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning

Zhang

Paudel

Wang

et al. 2019

The World Wide Web Conference

153

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Reasoning is essential for the development of large knowledge graphs, especially for completion, which aims to infer new triples based on existing ones. Both rules and embeddings can be used for knowledge graph reasoning and they have their own advantages and difficulties. Rule-based reasoning is accurate and explainable but rule learning with searching over the graph always suffers from efficiency due to huge search space. Embedding-based reasoning is more scalable and efficient as the reasoning is conducted via computation between embeddings, but it has difficulty learning good representations for sparse entities because a good embedding relies heavily on data richness. Based on this observation, in this paper we explore how embedding and rule learning can be combined together and complement each other's difficulties with their advantages. We propose a novel framework IterE iteratively learning embeddings and rules, in which rules are learned from embeddings with proper pruning strategy and embeddings are learned from existing triples and new triples inferred by rules. Evaluations on embedding qualities of IterE show that rules help improve the quality of sparse entity embeddings and their link prediction results. We also evaluate the efficiency of rule learning and quality of rules from IterE compared with AMIE+, showing that IterE is capable of generating high quality rules more efficiently. Experiments show * Work done while at UZH.

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Semantically Smooth Knowledge Graph Embedding

Cited by 126 publications

References 23 publications

A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications

A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications

Encoding Temporal Information for Time-Aware Link Prediction

Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning

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