Representation Learning of Knowledge Graphs with Entity Descriptions

Representation learning (RL) of knowledge graphs aims to project both entities and relations into a continuous low-dimensional space. Most methods concentrate on learning representations with knowledge triples indicating relations between entities. In fact, in most knowledge graphs there are usually concise descriptions for entities, which cannot be well utilized by existing methods. In this paper, we propose a novel RL method for knowledge graphs taking advantages of entity descriptions. More specifically, we explore two encoders, including continuous bag-of-words and deep convolutional neural models to encode semantics of entity descriptions. We further learn knowledge representations with both triples and…

• NR
  National Research Foundation
• NR
  National Research Foundation Singapore
• NN
  National Natural Science Foundation of China

  Awards: 61133012, 61572273
• NK
  National Key Research and Development Program of China

  Award: 2014CB340501