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Data Science Foundations: Knowledge Graphs

Data Science Foundations: Knowledge Graphs

31mIntermediate2021-07-29

Authors

Daniel Burgwinkel

Daniel Burgwinkel

Lecturer and Leader in Blockchain Programming

Course details

The term “knowledge graph” describes a semantic search based on the systematic compilation and processing of data and was first coined by Google. Leading internet companies have been using knowledge graphs for several years to present information that is tailored to customers’ needs. You can also use knowledge graphs to map your company’s internal knowledge and improve search results. Knowledge graphs can also improve the results of AI or machine learning systems. In this course, blockchain technology leader Daniel Burgwinkel explains what knowledge graphs are, offers examples and use cases, gives you practical recommendations on how to implement knowledge graphs, and shows you how to build a knowledge base. This course is aimed at data stewards, digital transformation managers, and data scientists who are responsible for data stocks and knowledge management.

Skills covered

Data VisualizationFoundationsData ScienceBusiness Analysis and StrategyBusiness Software and Tools

Concepts

Introduction

  • Knowledge graphs in corporate use

What Are Knowledge Graphs

  • Knowledge graphs in use by digital companies
  • Google Knowledge Graph
  • Knowledge graphs in media and ecommerce
  • What is a knowledge graph

Use Cases for Knowledge Graphs

  • Knowledge management
  • Recommender engines
  • Machine learning with knowledge graphs

Industry-Specific Knowledge Graphs

  • Knowledge graphs in healthcare
  • Sector-specific knowledge models
  • Text mining and machine learning

Recommendations for Implementation

  • Benefits of knowledge graphs in digital transformation
  • Checklist for the use of knowledge graphs in an organization
  • Create use-case ideas for knowledge graphs

How to Build a Knowledge Base

  • Create a taxonomy or data catalog
  • Create an ontology
  • Build the knowledge base
  • Make knowledge usable internally and externally

Conclusion

  • Further information

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