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Knowledge Graph Representation via Similarity-Based Embedding
Joint Authors
Zhao, Xiang
Tan, Zhen
Fang, Yang
Ge, Bin
Xiao, Weidong
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-07-15
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Knowledge graph, a typical multi-relational structure, includes large-scale facts of the world, yet it is still far away from completeness.
Knowledge graph embedding, as a representation method, constructs a low-dimensional and continuous space to describe the latent semantic information and predict the missing facts.
Among various solutions, almost all embedding models have high time and memory-space complexities and, hence, are difficult to apply to large-scale knowledge graphs.
Some other embedding models, such as TransE and DistMult, although with lower complexity, ignore inherent features and only use correlations between different entities to represent the features of each entity.
To overcome these shortcomings, we present a novel low-complexity embedding model, namely, SimE-ER, to calculate the similarity of entities in independent and associated spaces.
In SimE-ER, each entity (relation) is described as two parts.
The entity (relation) features in independent space are represented by the features entity (relation) intrinsically owns and, in associated space, the entity (relation) features are expressed by the entity (relation) features they connect.
And the similarity between the embeddings of the same entities in different representation spaces is high.
In experiments, we evaluate our model with two typical tasks: entity prediction and relation prediction.
Compared with the state-of-the-art models, our experimental results demonstrate that SimE-ER outperforms existing competitors and has low time and memory-space complexities.
American Psychological Association (APA)
Tan, Zhen& Zhao, Xiang& Fang, Yang& Ge, Bin& Xiao, Weidong. 2018. Knowledge Graph Representation via Similarity-Based Embedding. Scientific Programming،Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1214723
Modern Language Association (MLA)
Tan, Zhen…[et al.]. Knowledge Graph Representation via Similarity-Based Embedding. Scientific Programming No. 2018 (2018), pp.1-12.
https://search.emarefa.net/detail/BIM-1214723
American Medical Association (AMA)
Tan, Zhen& Zhao, Xiang& Fang, Yang& Ge, Bin& Xiao, Weidong. Knowledge Graph Representation via Similarity-Based Embedding. Scientific Programming. 2018. Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1214723
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1214723