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Learning Embedding Representations for Knowledge Inference on Imperfect and Incomplete Repositories
[article]
2015
arXiv
pre-print
This paper considers the problem of knowledge inference on large-scale imperfect repositories with incomplete coverage by means of embedding entities and relations at the first attempt. We propose IIKE (Imperfect and Incomplete Knowledge Embedding), a probabilistic model which measures the probability of each belief, i.e. 〈 h,r,t〉, in large-scale knowledge bases such as NELL and Freebase, and our objective is to learn a better low-dimensional vector representation for each entity (h and t) and
arXiv:1503.08155v1
fatcat:3lxeq6n2rva7tgyrkdc2siz3gq