Top 10 Textbooks on Knowledge Graphs

By: Anish Khan

Machine learning continues to draw researchers to a new learning paradigm with each passing year. Machine learning has progressed dramatically in recent years. Google invented the phrase “Knowledge Graph” in 2012 as a new technical term. Knowledge graphs can be defined as a graphical representation of data points from different data sets to show the correlation between them [1]. For example, the accuracy rate of machine learning methods improves with each passing day because of this knowledge graph’s importance in the area. [2] Knowledge Graphs and their real-time applications with the integration of Machine Learning [3-4] and Artificial Intelligence will be the focus of recent research development.

Knowledge Graphs and Big Data Processing
Authors: Valentina Janev et al.

Knowledge Graphs

Practical Graph Mining with R
Authors: Arpan Chakraborty

Graph-Based Semi-Supervised Learning
Authors: Amarnag Subramanya et al.

Graph Algorithms and Applications 3
Authors: Giuseppe Liotta et al.

New Frontiers in Graph Theory
Authors: Yagang Zhang

Pearls in Graph Theory: A Comprehensive Introduction
Authors: Nora Hartsfield, Gerhard Ringel


  1. A. Khan, F. Colace (2021) Knowledge Graph: Applications with ML and AI and Open-Source Database Links in 2022, Insights2Techinfo
  2. Wang, Q., Mao, Z., Wang, B., & Guo, L. (2017). Knowledge graph embedding: A survey of approaches and applicationsIEEE Transactions on Knowledge and Data Engineering29(12), 2724-2743.
  3. K. Yadav, M. Quamara, B. Gupta (2021), 2021 Hot Topics in Machine Learning Research, Insights2Techinfo, pp.1
  4. A. Khan, K. T. Chui, D. Peraković (2021) Future Scope of AI and Machine Learning in 2022, Insights2Techinfo, pp.1

Cite this article as:

Anish Khan (2021) Top 10 Textbooks on Knowledge Graphs, Insights2Techinfo, pp.1

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