On Completing Sparse Knowledge Base with Transitive Relation Embedding

  • Zili Zhou University of Technology Sydney
  • Shaowu Liu University of Technology Sydney
  • Guandong Xu University of Technology Sydney
  • Wu Zhang Shanghai University

Abstract

Multi-relation embedding is a popular approach to knowledge base completion that learns embedding representations of entities and relations to compute the plausibility of missing triplet. The effectiveness of embedding approach depends on the sparsity of KB and falls for infrequent entities that only appeared a few times. This paper addresses this issue by proposing a new model exploiting the entity-independent transitive relation patterns, namely Transitive Relation Embedding (TRE). The TRE model alleviates the sparsity problem for predicting on infrequent entities while enjoys the generalisation power of embedding. Experiments on three public datasets against seven baselines showed the merits of TRE in terms of knowledge base completion accuracy as well as computational complexity.

Published
2019-07-17
Section
AAAI Technical Track: Knowledge Representation and Reasoning