Developing Knowledge Graphs in the Enterprise

The course will be of most value to:

Data practitioners: Aspiring or practicing data scientists, data engineers or data analysts, seeking to deepen their understanding of knowledge graphs, their implementation, and the technical intricacies involved.

Technology Leaders: Architects, CTOs , and IT professionals exploring or leading initiatives involving data integration, semantic technologies, and knowledge management systems.

By the end of this live online 2-day course, you’ll be able to:

• Decide whether a knowledge graph is a proper solution for your data challenges, and specify its desired characteristics.
• Understand the key factors determining the feasibility and viability of implementing a knowledge graph in an organization, and craft a proper development strategy
• Apply techniques to determine and prioritize the content requirements of a knowledge graph.
• Design a knowledge graph’s schema in a way that makes the rest of the graph’s development much easier.
• Apply state-of-the-art tools and methods to automatically populate a knowledge graph from diverse data sources.
• Implement mechanisms to assess and improve the quality of a knowledge graph
• Apply knowledge graphs in practical application scenarios such as question answering and semantic data analytics.
• Design and implement a knowledge graph evolution and governance strategy.

In today's data-driven landscape, the concept of a knowledge graph has emerged as a pivotal framework for managing and utilizing interconnected data and information. Stemming from Google's proclamation that shifted the focus from searching for strings to understanding entities and relationships, the term encapsulates a network of interconnected entities and concepts, facilitating data integration, sharing, and utilization within organizations.

Implementing a successful Knowledge Graph initiative within an organization demands strategic decisions before and during its execution. Often overlooked are critical considerations such as managing trade-offs between knowledge quality and other factors, prioritizing knowledge evolution, and allocating resources effectively. Neglecting these facets can lead to friction and suboptimal outcomes.

This 2-day online course delves into the technical, business, and organizational dimensions essential for data practitioners and executives embarking on a Knowledge Graph initiative. The course covers all the stages of knowledge graph development in an organizational setting, including crafting a development strategy, developing the graph schema, populating the graph with data, controlling its quality, putting the graph into use, and managing its evolution . Offering insights gleaned from real-world case studies, the course provides a comprehensive framework that combines cutting-edge techniques with pragmatic advice, and equips participants to navigate the complexities of executing a knowledge graph project successfully.

This course does not include an exam.

The course will walk participants through 8 key stages of introducing, developing, delivering and evolving Knowledge Graphs in an organization. These are:

Stage 1 – “Knowing where you are getting into”

• Clarification of the knowledge graph concept
• What are knowledge graphs and why would you want to build one
• Key factors influencing the ease or difficulty of building a knowledge graph
• Knowledge graph development lifecycle;

Stage 2 – ”Setting up the stage”

• Exploring 5 key questions that are essential to answer before initiating knowledge graph development
• How to answer these questions and craft a development strategy

Stage 3 – “Deciding what to build”:

• Delving into knowledge graph specification
• Use of competency questions for gap analysis between organizational knowledge capabilities and needs
• Scoping and prioritizing knowledge graph content

Stage 4 – “Giving it a shape”

• Building elements of a knowledge graph
• Knowledge graph schema design using Semantic Web languages (RDF, OWL, SKOS, SHACL) and Labeled Property Graphs.
• Conceptual modeling best practices, dilemmas, and pitfalls
• Addressing uncertainty and vagueness

Stage 5 – “Giving it substance”

• Understanding and defining knowledge graph population tasks such as entity and relation extraction
• Evaluating and selecting data sources and population tools
• Designing population strategies and developing relevant population pipelines using state-of-the-art NLPand machine-learning techniques.

Stage 6 – “Ensuring it’s good”:

• Knowledge graph quality dimensions, metrics, and trade-offs.
• Typical quality problems and debugging methods
• Detecting and correct quality problems in knowledge graphs using state-of-the-art NLP and Machine Learning techniques.
• Crafting a quality management strategy

Stage 7 – “Making it useful”:

• Typical knowledge graph applications
• Developing analytics solutions with knowledge graphs
• Using knowledge graphs to ground Large Language Models

Stage 8 – “Making it last”:

• Addressing the challenge of knowledge graph maintenance and evolution
• Detecting, measuring, and monitoring concept drift
• Crafting a knowledge graph governance strategy.