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 . For example, the accuracy rate of machine learning methods improves with each passing day because of this knowledge graph’s importance in the area.  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 Methodology, Tools and Selected Use Cases
Authors: Dieter Fensel et al.
Knowledge Graphs and Big Data Processing
Authors: Valentina Janev et al.
Graph Theory with Applications to Engineering and Computer Science
Authors: Narsingh Deo
Domain-Specific Knowledge Graph Construction
Authors: Mayank Kejriwal
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.
Graph Theory Advanced Algorithms and Applications
Authors: Beril Sirmacek
New Frontiers in Graph Theory
Authors: Yagang Zhang
Pearls in Graph Theory: A Comprehensive Introduction
Authors: Nora Hartsfield, Gerhard Ringel
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Cite this article as:
Anish Khan (2021) Top 10 Textbooks on Knowledge Graphs, Insights2Techinfo, pp.1