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Knowledge Graph Representation via Similarity-Based Embedding
المؤلفون المشاركون
Zhao, Xiang
Tan, Zhen
Fang, Yang
Ge, Bin
Xiao, Weidong
المصدر
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-07-15
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1214723
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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