This article introduces part 5 of the Introduction to knowledge graphs series of articles. Recent research has identified the development of knowledge graphs as an important aspect of artificial intelligence (AI) in knowledge management (KM).
- 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, continuing with inductive knowledge in this part (part 5) as shown below. Additional information from other relevant reference sources has been added to some sections.
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.
Next part: (section 5.1): Inductive knowledge – Graph analytics.
- 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. ↩