  Knowledge representation learning (KRL) aims to represent entities and
relations in knowledge graph in low-dimensional semantic space, which have been
widely used in massive knowledge-driven tasks. In this article, we introduce
the reader to the motivations for KRL, and overview existing approaches for
KRL. Afterwards, we extensively conduct and quantitative comparison and
analysis of several typical KRL methods on three evaluation tasks of knowledge
acquisition including knowledge graph completion, triple classification, and
relation extraction. We also review the real-world applications of KRL, such as
language modeling, question answering, information retrieval, and recommender
systems. Finally, we discuss the remaining challenges and outlook the future
directions for KRL. The codes and datasets used in the experiments can be found
in https://github.com/thunlp/OpenKE.
