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Article

A Survey on Knowledge Graph Embedding: Approaches, Applications and Benchmarks

1
College of Mathematics and Computer Sciences, Fuzhou University, Fuzhou 350108, China
2
Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou 350108, China
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Department of Mathematics and Computer Science, Northeastern State University, Tahlequah, OK 003161, USA
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(5), 750; https://doi.org/10.3390/electronics9050750
Received: 4 April 2020 / Revised: 25 April 2020 / Accepted: 29 April 2020 / Published: 2 May 2020
(This article belongs to the Special Issue Data Analysis in Intelligent Communication Systems)
A knowledge graph (KG), also known as a knowledge base, is a particular kind of network structure in which the node indicates entity and the edge represent relation. However, with the explosion of network volume, the problem of data sparsity that causes large-scale KG systems to calculate and manage difficultly has become more significant. For alleviating the issue, knowledge graph embedding is proposed to embed entities and relations in a KG to a low-, dense and continuous feature space, and endow the yield model with abilities of knowledge inference and fusion. In recent years, many researchers have poured much attention in this approach, and we will systematically introduce the existing state-of-the-art approaches and a variety of applications that benefit from these methods in this paper. In addition, we discuss future prospects for the development of techniques and application trends. Specifically, we first introduce the embedding models that only leverage the information of observed triplets in the KG. We illustrate the overall framework and specific idea and compare the advantages and disadvantages of such approaches. Next, we introduce the advanced models that utilize additional semantic information to improve the performance of the original methods. We divide the additional information into two categories, including textual descriptions and relation paths. The extension approaches in each category are described, following the same classification criteria as those defined for the triplet fact-based models. We then describe two experiments for comparing the performance of listed methods and mention some broader domain tasks such as question answering, recommender systems, and so forth. Finally, we collect several hurdles that need to be overcome and provide a few future research directions for knowledge graph embedding. View Full-Text
Keywords: knowledge graph embedding; knowledge representation; deep learning; statistical relational learning knowledge graph embedding; knowledge representation; deep learning; statistical relational learning
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MDPI and ACS Style

Dai, Y.; Wang, S.; Xiong, N.N.; Guo, W. A Survey on Knowledge Graph Embedding: Approaches, Applications and Benchmarks. Electronics 2020, 9, 750. https://doi.org/10.3390/electronics9050750

AMA Style

Dai Y, Wang S, Xiong NN, Guo W. A Survey on Knowledge Graph Embedding: Approaches, Applications and Benchmarks. Electronics. 2020; 9(5):750. https://doi.org/10.3390/electronics9050750

Chicago/Turabian Style

Dai, Yuanfei, Shiping Wang, Neal N. Xiong, and Wenzhong Guo. 2020. "A Survey on Knowledge Graph Embedding: Approaches, Applications and Benchmarks" Electronics 9, no. 5: 750. https://doi.org/10.3390/electronics9050750

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