Developing Knowledge Graphs in the Enterprise

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. Critical considerations are often overlooked, 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.

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Who it this Course for?

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.

Why take this Course?

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.
About this Course

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.

Is there an Exam?

This course does not include an exam.

Full Course Outline

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.
Panos Alexopoulos
Founder and Principal Educator
OWLTECH 
Panos Alexopoulos has been working since 2006 at the intersection of data, semantics and software, contributing in building intelligent systems that deliver value to business and society. Born and raised in Athens, Greece, Panos currently works as a principal educator & consultant at OWLTECH, developing and delivering training workshops that provide actionable knowledge and insights for data and AI practitioners. He also works as Head of Ontology at Textkernel BV, in Amsterdam, Netherlands, leading a team of data professionals in developing and delivering a large cross-lingual Knowledge Graph in the HR and Recruitment domain.  Panos has published several papers at international conferences, journals and books, and he is a regular speaker in both academic and industry venues, striving to bridge the gap between academia and industry so that they can benefit from each other. He is also the author of the O’Reilly book “Semantic Modeling for Data – Avoiding Pitfalls and Dilemmas”, a practical and pragmatic field guide for data practitioners that want to learn how semantic data modeling is applied in the real world.
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