A Survey on Knowledge Graphs: Representation, Acquisition, and Applications

Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, Philip S. Yu

Research output: Contribution to journalArticleScientificpeer-review

245 Citations (Scopus)


Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction toward cognition and human-level intelligence. In this survey, we provide a comprehensive review of the knowledge graph covering overall research topics about: 1) knowledge graph representation learning; 2) knowledge acquisition and completion; 3) temporal knowledge graph; and 4) knowledge-aware applications and summarize recent breakthroughs and perspective directions to facilitate future research. We propose a full-view categorization and new taxonomies on these topics. Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models, and auxiliary information. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning are reviewed. We further explore several emerging topics, including metarelational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of data sets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions.

Original languageEnglish
Pages (from-to)494-514
Number of pages21
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number2
Early online date24 Apr 2021
Publication statusPublished - Feb 2022
MoE publication typeA1 Journal article-refereed


  • Cognition
  • Deep learning
  • Extraterrestrial measurements
  • Knowledge acquisition
  • Knowledge based systems
  • knowledge graph
  • knowledge graph completion (KGC)
  • reasoning
  • relation extraction
  • representation learning.
  • Semantics
  • Task analysis
  • Taxonomy


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