Knowledge graph embeddings
How can we use knowledge graph in computing? A knowledge graph is a symbolic and logical system while applications often involve numerical computing in continuous spaces. Formal logic is neither tractable nor robust when dealing with knowledge graph. Hence the idea of Knowledge graph embeddings. A knowledge graph is embedded into a low-dimensional continuous vector space while certain properties of it are preserved ([Bordes et al., 2013](http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-rela), Socher et al., 2013; Chang et al., 2013; Wang et al., 2014). Generally, each entity is represented as a point in that space while each relation is interpreted as an operation over entity embeddings ((eg. in Bordes et al., a translation). The embedding representations are usually learnt by minimizing a global loss function involving all entities and relations so that each entity embedding encodes both local and global connectivity patterns of the original graph. Thus, we can reason new facts from learnt embeddings
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