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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<jats:sans-serif> TransE<\/jats:sans-serif> and<jats:sans-serif> DistMult<\/jats:sans-serif>, 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,<jats:sans-serif> SimE<\/jats:sans-serif>-<jats:sans-serif>ER<\/jats:sans-serif>, to calculate the similarity of entities in independent and associated spaces. In<jats:sans-serif> SimE<\/jats:sans-serif>-<jats:sans-serif>ER<\/jats:sans-serif>, 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<jats:sans-serif> SimE<\/jats:sans-serif>-<jats:sans-serif>ER<\/jats:sans-serif> outperforms existing competitors and has low time and memory-space complexities.<\/jats:p>","DOI":"10.1155\/2018\/6325635","type":"journal-article","created":{"date-parts":[[2018,7,15]],"date-time":"2018-07-15T19:32:27Z","timestamp":1531683147000},"page":"1-12","source":"Crossref","is-referenced-by-count":2,"title":["Knowledge Graph Representation via Similarity-Based Embedding"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8643-4683","authenticated-orcid":true,"given":"Zhen","family":"Tan","sequence":"first","affiliation":[{"name":"Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, Hunan 410073, 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