Data Science Foundations: Knowledge Graphs

Data Science Foundations: Knowledge Graphs

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 0h 31m | 125 MB

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.

Table of Contents

1 Knowledge graphs in corporate use

1. What Are Knowledge Graphs
2 Knowledge graphs in use by digital companies
3 Google Knowledge Graph
4 Knowledge graphs in media and ecommerce
5 What is a knowledge graph

2. Use Cases for Knowledge Graphs
6 Knowledge management
7 Recommender engines
8 Machine learning with knowledge graphs

3. Industry-Specific Knowledge Graphs
9 Knowledge graphs in healthcare
10 Sector-specific knowledge models
11 Text mining and machine learning

4. Recommendations for Implementation
12 Benefits of knowledge graphs in digital transformation
13 Checklist for the use of knowledge graphs in an organization
14 Create use-case ideas for knowledge graphs

5. How to Build a Knowledge Base
15 Create a taxonomy or data catalog
16 Create an ontology
17 Build the knowledge base
18 Make knowledge usable internally and externally

19 Further information