Knowledge Graphs: Fundamentals, Techniques, and Applications

Book Description A rigorous and comprehensive textbook covering the major approaches to knowledge graphs, an active and interdisciplinary area within artificial intelligence.

The field of knowledge graphs, which allows us to model, process, and derive insights from complex real-world data, has emerged as an active and interdisciplinary area of artificial intelligence over the last decade, drawing on such fields as natural language processing, data mining, and the semantic web. Current projects involve predicting cyberattacks, recommending products, and even gleaning insights from thousands of papers on COVID-19. This textbook offers rigorous and comprehensive coverage of the field. It focuses systematically on the major approaches, both those that have stood the test of time and the latest deep learning methods.

Content

I - KNOWLEDGE GRAPH FUNDAMENTALS
1. Introduction to Knowledge Graphs
2. Modeling and Representing Knowledge Graphs

II - KNOWLEDGE GRAPH CONSTRUCTION
3. Domain Discovery
4. Named Entity Recognition
5. Web Information Extraction
6. Relation Extraction
7. Nontraditional Information Extraction

III - KNOWLEDGE GRAPH COMPLETION
8. Instance Matching
9. Statistical Relational Learning

IV - ACCESSING KNOWLEDGE GRAPHS
11. Reasoning and Retrieval
12. Structured Querying
13. Question Answering

V - KNOWLEDGE GRAPH ECOSYSTEMS
14. Linked Data
15. Enterprise and Government
16. Knowledge Graphs and Ontologies in Science
17. Knowledge Graphs for Domain-Specific Social Impact