Integrated Embedding Approach for Knowledge Base Completion with CNN

Abstract

To tackle specific problems in knowledge base completion such as computational complexity and complex relations or nodes with high indegree or outdegree, an algorithm called IEAKBC(short for Integrated Embedding Approach for Knowledge Base Completion) is proposed, in which entities and relations from triplets are first mapped into low-dimensional vector spaces, each original triplet represented in the form of 3-column, k dimensional matrix; then features from different relations are integrated into head and tail entities thus forming fused triplet matrices used as another input channel for convolution. In CNN feature maps are extracted by filters, concatenated and weighted for output scores to discern whether the original triplet holds or not. Experiments show that IEAKBC holds certain advantages over other models; when scaling up to relatively larger datasets, signs of superiority of IEAKBC stand out especially on relations with high cardinalities. At last we apply IEAKBC to a personalized search application, comparing its performance with strong baselines to verify its practicality in real environments.