ABCs of KMIntroduction to knowledge graphs

Introduction to knowledge graphs (part 3): Data graphs

This article introduces part 3 of the Introduction to knowledge graphs series of articles.

In their comprehensive multi-author tutorial article1, Aidan Hogan and colleagues:

  • outline graph data models and the languages used to query and validate them
  • present deductive formalisms by which knowledge can be represented and entailed
  • describe inductive techniques by which additional knowledge can be extracted.

Hogan and colleagues’ article is summarised in parts 3, 4, and 5 of this series, beginning with data graphs in this part (part 3) as shown below. Additional information from other relevant reference sources has been added to some sections.

Tourism example

To keep the discussion accessible, Hogan and colleagues’ present concrete examples for a hypothetical knowledge graph, which are reproduced in the parts 3, 4, and 5 of this series.  This hypothetical knowledge graph relates to tourism in Chile, aiming to increase tourism in the country and promote new attractions in strategic areas through an online tourist information portal. The knowledge graph itself will eventually describe tourist attractions, cultural events, services, businesses, as well as cities and popular travel routes.

Part 3 – Data graphs

At the foundation of any knowledge graph is the principle of first modelling data as a graph. This part discusses a selection of popular graph-structured data models, languages used to query and validate graphs, as well as representations of context in graphs.

Section 3.1 – Models

Section 3.2 – Querying

Section 3.3 – Validation

Section 3.4 – Context

Next part: (section 3.1): Data graphs – Models.

Header image source: Crow Intelligence, CC BY-NC-SA 4.0.


  1. Hogan, A., Blomqvist, E., Cochez, M., d’Amato, C., Melo, G. D., Gutierrez, C., … & Zimmermann, A. (2021). Knowledge graphs. ACM Computing Surveys (CSUR), 54(4), 1-37.
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Bruce Boyes

Bruce Boyes ( is editor, lead writer, and a director of the award-winning RealKM Magazine (, and a knowledge management (KM), environmental management, and project management professional. He is a PhD candidate in the Knowledge, Technology and Innovation Group at Wageningen University and Research, and holds a Master of Environmental Management with Distinction. His expertise and experience includes knowledge management (KM), environmental management, project management, stakeholder engagement, teaching and training, communications, research, and writing and editing. With a demonstrated ability to identify and implement innovative solutions to social and ecological complexity, Bruce's many career highlights include establishing RealKM Magazine as an award-winning resource, using agile and knowledge management approaches to oversee an award-winning $77.4 million western Sydney river recovery program, leading a knowledge strategy process for Australia's 56 natural resource management (NRM) regional organisations, pioneering collaborative learning and governance approaches to support the sustainable management of landscapes and catchments, and initiating and teaching two new knowledge management subjects at Shanxi University in China.

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