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Link prediction for knowledge graphs is the task of predicting missing relationships between entities. Previous work on link… Expand We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of… Expand In the recent years, different Web knowledge graphs, both free and commercial, have been created. While Google coined the term… Expand Knowledge graph (KG) embedding is to embed components of a KG including entities and relations into continuous vector spaces, so… Expand Machine learning about language can be improved by supplying it with specific knowledge and sources of external information. We… Expand Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data… Expand Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. In this paper… Expand Knowledge graph completion aims to perform link prediction between entities. In this paper, we consider the approach of knowledge… Expand Knowledge graphs are useful resources for numerous AI applications, but they are far from completeness. Previous work such as… Expand We deal with embedding a large scale knowledge graph composed of entities and relations into a continuous vector space. TransE is… Expand