只是图片综述

• 知识图谱综述
  • Abstract
  • Introduction
  • 2. Overview
    • 2.1 A Brief History of Knowledge Bases
  • 2.2 Definitions and Notations
  • Reference

知识图谱综述

https://segmentfault.com/a/1190000016522687

Abstract

• knowledge graph representation learning：知识表示学习
• knowledge acquisition and completion：知识获取与补全

• temporal knowledge graph：时序知识图谱

• knowledge-aware applications：知识图谱应用

• summarize recent breakthroughs and perspective directions to facilitate future research

Introduction

知识图谱包含：

• entities，can be real-world objects and abstract concepts
• relationship，represent the relation between entities
• semantic descriptions：semantic descriptions of entities and their relationships contain types and properties with a well-defined meaning.

本文内容：

• Comprehensive review. We conduct a comprehensive review on the origin of knowledge graph and modern techniques for relational learning on knowledge graphs. Major neural architectures of knowledge graph representation learning and reasoning are introduced and compared. Moreover, we provide a complete overview of many applications on different domains.

• Full-view categorization and new taxonomies. A full-view categorization of research on knowledge graph, together with fine-grained new taxonomies are presented.

  Specifically, in the high-level we review knowledge graph in three aspects: KRL, knowledge acquisition, and knowledge-aware application.

  • For KRL approaches, we further propose fine-grained taxonomies into four views including representa tion space, scoring function, encoding models, and auxiliary information.
  • For knowledge acquisition, KGC is reviewed under embedding-based ranking, relational path reasoning, logical rule reasoning and meta relational learning; entity-relation acquisition tasks are divided into entity recognition, typing, disambiguation, and alignment; and relation extraction is discussed according to the neural paradigms.

• Wide coverage on emerging advances. This survey provides a wide coverage on emerging topics including transformer-based knowledge encoding, graph neural network (GNN) based knowledge prop- agation, reinforcement learning based path reasoning, and meta relational learning.

• Summary and outlook on future directions. This survey provides a summary on each category and highlights promising future research directions.

2. Overview

2.1 A Brief History of Knowledge Bases

2.2 Definitions and Notations

Reference

[1] A Survey on Knowledge Graphs: Representation, Acquisition and Applications