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Knowledge Graphs: Opportunities and Challenges.

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Knowledge graphs organize real-world information for artificial intelligence (AI) systems. This paper surveys AI opportunities and technical challenges in knowledge graph development.

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Area of Science:

  • Artificial Intelligence
  • Data Science
  • Knowledge Representation

Background:

  • The rapid expansion of artificial intelligence (AI) and big data necessitates effective knowledge organization.
  • Knowledge graphs, as a form of graph data, are crucial for representing real-world information and complex relationships.
  • There is growing academic and industrial interest in knowledge graphs due to their representational power.

Purpose of the Study:

  • To provide a systematic overview of the field of knowledge graphs.
  • To explore the opportunities presented by knowledge graphs, particularly in AI systems and diverse application domains.
  • To identify and discuss significant technical challenges in knowledge graph research and development.

Main Methods:

  • Systematic literature review focusing on knowledge graphs.
  • Analysis of opportunities, categorized into AI system integration and application fields.
  • Discussion of key technical challenges including embeddings, acquisition, completion, fusion, and reasoning.

Main Results:

  • Knowledge graphs offer significant opportunities for building advanced AI systems.
  • Diverse application fields are emerging for knowledge graph technology.
  • Several critical technical challenges impede the full realization of knowledge graph potential.

Conclusions:

  • Knowledge graphs are pivotal for managing and leveraging vast amounts of information in the AI era.
  • Addressing challenges in knowledge graph embeddings, acquisition, completion, fusion, and reasoning is essential for future progress.
  • This survey aims to guide future research and development in the dynamic field of knowledge graphs.