Knowledge Graphs – pragmatic approach and best practices [English spoken]

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.

Amid the widespread adoption of knowledge graphs across diverse domains, ensuring the accuracy, reliability, and consensus of semantic information becomes an imperative. The construction and utilization of these graphs present multifaceted challenges, ranging from ensuring data quality to scaling and adapting to evolving contexts.

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 half-day seminar delves into the technical, business, and organizational dimensions essential for data practitioners and executives embarking on a Knowledge Graph initiative. Offering insights gleaned from real-world case studies, the seminar provides a comprehensive framework that combines cutting-edge techniques with pragmatic advice. It equips participants to navigate the complexities of executing a knowledge graph project successfully.

Moreover, the session addresses pivotal strategic dilemmas encountered during the design and execution phases of knowledge graph projects, and outlines potential approaches to tackle these challenges, empowering attendees with actionable strategies to optimize their initiatives.

Learning Objectives

• Understand the key factors determining the feasibility and viability of implementing a knowledge graph in an organization.
• Identify and articulate the fundamental questions crucial for preparing and launching a successful knowledge graph initiative.
• Learn techniques to determine and prioritize the content requirements of a knowledge graph.
• Grasp best practices in schema design for knowledge graphs, addressing real-world challenges of uncertainty and vagueness.
• Explore strategies and guidelines for populating a knowledge graph, evaluating available knowledge extraction systems.
• Gain insights into assessing and prioritizing quality dimensions within a knowledge graph.
• Explore practical applications of knowledge graphs, such as entity disambiguation and semantic search, optimizing performance through design principles.
• Gain insights into methodologies for ongoing maintenance and evolution of knowledge graphs, ensuring their sustained relevance and adaptability across time.

Who is it for?

• Data practitioners: Data scientists, data engineers, data analysts, and database administrators 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.
• Business Executives and Managers: Leaders and decision-makers responsible for overseeing data strategies, innovation, and organizational transformation, aiming to comprehend the strategic implications and business value derived from knowledge graph initiatives.

Course Outline

The seminar 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
• Key factors influencing the ease or difficulty of building a knowledge graph
• Evaluating feasibility and viability of implementing a knowledge graph in a specific organization and for a particular business problem

Stage 2 – ”Setting up the stage”

• Exploring 5 key questions essential before initiating knowledge graph development
• Defining what, why, how, who, and the stakeholders involved in the project
• Outlining actions required to seek and discover answers to these questions

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”

• Schema design using Ontology Representation and Engineering
• Identification of conceptual modeling best practices, dilemmas, and pitfalls
• Addressing uncertainty and vagueness

Stage 5 – “Giving it substance”

• Exploring the challenging task of knowledge graph population
• Description of population tasks and associated difficulties
• Designing optimal population pipelines

Stage 6 – “Ensuring it’s good”:

• Assessing knowledge graph quality, defining dimensions, and metrics
• Insights into quality trade-offs and prioritization of dimensions
• Measuring quality and effective prioritization of focus areas

Stage 7 – “Making it useful”:

• Typical knowledge graph applications
• Guidelines and best practices for optimizing knowledge graph usefulness and value

Stage 8 – “Making it last”:

• Addressing the challenge of knowledge graph maintenance and evolution
• Detecting, measuring, and monitoring concept drift
• Best practices for enabling continuous improvement and preventing knowledge graph obsolescence over time.