Knowledge Graph Architecture for the Enterprise

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4-hour 37-min Online Course by Dean Allemang
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Our Price: $575.00

Product Code: DM-06-A

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Course Outline

Exam Details

Knowledge Graph Architecture for the Enterprise: Fundamentals for Digital Transformation

Businesses today need to have comprehensive control and understanding of the data both within the enterprise and from outside. Regulatory pressure in many industries requires that an enterprise be able to find all relevant data about customers, products, business relations and in fact any aspect of the business quickly and accurately. Advances in data analytics have shown that the more meaningful data an enterprise can bring to bear, the more accurately business drivers can be predicted. For this reason, many businesses are migrating from viewing their enterprise information as a collection of data to viewing it as an ongoing graph of meaningful knowledge.

Presented in partnership with:

You will learn:

What a Knowledge Graph is, what are its uses, and how does it provide business value
Different approaches to Knowledge Graphs, and when to use them
Knowledge Graph standards and their importance in governance and application sustainability
Categories of tools for building, deploying and maintaining knowledge graphs, and how they fit together into a knowledge graph deployment
The relationship between knowledge and data in a knowledge graph, and how to manage them both
Best Practices for data publication, use and re-use

This course is geared towards:

Data integration architects, designers, and developers
Data and technology architects
Chief Data Officers

Module 0. About the Course (7 min)

Module 1. Definitions and Business Case (55 min)

History of Knowledge Graphs
Knowledge Graphs in Various Industries - Finance
Knowledge Graphs in Various Industries - Media
Knowledge Graphs in Various Industries - Agriculture
Knowledge Graphs in Various Industries - Pharmaceuticals
Data Management Challenges in the Enterprise

Module 2. Knowledge Graph Supporting Technologies (40 min)

Features of Graph Data Systems
Approaches to Graphs as Data
Property Graph Data Capabilities
Graph Data Queries
Graph Data Visualization
Graph Data Systems
Standardizing Data

Module 3. Semantic Technology Fundamentals (58 min)

Knowledge Graph Stack
RDF Brings Data Together
RDFS Enhance Data with Types & Properties
OWL Provides Precision Logic to Describe Models
SPARQL Lets you Ask Questions About the Data
Example Knowledge Graph Capabilities Using Semantic Web Standards
Using SKOS for Knowledge Management
The Meaning of Meaning

Module 4. Knowledge Graph Enterprise Framework (35 min)

Application Vs. Enterprise Data
The Enterprise Data Jungle
Sustainable Extensibility
Enterprise Data Community
Prerequisites for Distributed Data
Common Reference
Connecting References
Semantic Alignment
Things vs. Strings
Tools and Components
Applications of Knowledge Graphs

Module 5. Modeling Methodology and Architecture (33 min)

Modeling Methodology and Architecture
Terminology Sources
Guidelines for Terminology

Module 6. Implementation Fundamentals of Knowledge Graph (47 min)

Example Implementation
Incremental Development
Knowledge Graph Implementation
Presentation Approach
Select a Use case
Inventory Data Sources
Draw on Reference Ontologies
Identify Controlled Vocabulary
Map Metadata to Ontologies
Materialize Data as Needed
Build Queries to Respond to Business Questions
Repeat from Step 3 with New Data
How Can This Fail?
Summary: Incremental Development

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