Learning Embedding Representations for Knowledge Inference on Imperfect and Incomplete Repositories
This addresses the problem of handling noisy and incomplete knowledge bases for AI researchers, representing an incremental advancement in embedding methods.
The paper tackles knowledge inference on imperfect and incomplete repositories by proposing IIKE, a probabilistic embedding model that learns vector representations for entities and relations, achieving significant improvement over baseline and state-of-the-art approaches in link prediction and triplet classification experiments.
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. $\langle h,r,t\rangle$, 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 relation ($r$) in the process of minimizing the loss of fitting the corresponding confidence given by machine learning (NELL) or crowdsouring (Freebase), so that we can use $||{\bf h} + {\bf r} - {\bf t}||$ to assess the plausibility of a belief when conducting inference. We use subsets of those inexact knowledge bases to train our model and test the performances of link prediction and triplet classification on ground truth beliefs, respectively. The results of extensive experiments show that IIKE achieves significant improvement compared with the baseline and state-of-the-art approaches.