2015/03/27 by Miao Fan, Qiang Zhou, Fan, Miao +3 · 2 citations
Computer Science · #Advanced Graph Neural Networks #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Topic Modeling #cs.AI #cs.CL
paper · pdf · doi:10.48550/arxiv.1503.08155
arxiv created 2015/03/27 · openalex publication_date 2015/03/27 · arxiv updated 2015/03/30 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/02
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 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.