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This paper considers the problem of knowledge inference on large-scaleimperfect repositories with incomplete coverage by means of embedding entitiesand relations at the first attempt. We propose IIKE (Imperfect and IncompleteKnowledge Embedding), a probabilistic model which measures the probability ofeach belief, i.e. $\langle h,r,t\rangle$, in large-scale knowledge bases suchas NELL and Freebase, and our objective is to learn a better low-dimensionalvector representation for each entity ($h$ and $t$) and relation ($r$) in theprocess of minimizing the loss of fitting the corresponding confidence given bymachine learning (NELL) or crowdsouring (Freebase), so that we can use $||{\bfh} + {\bf r} - {\bf t}||$ to assess the plausibility of a belief whenconducting inference. We use subsets of those inexact knowledge bases to trainour model and test the performances of link prediction and tripletclassification on ground truth beliefs, respectively. The results of extensiveexperiments show that IIKE achieves significant improvement compared with thebaseline and state-of-the-art approaches.

Learning Embedding Representations for Knowledge Inference on Imperfect and Incomplete Repositories