ABCs of KMFeatured StoriesIntroduction to knowledge graphs

Introduction to knowledge graphs (part 6): Summary and conclusion

This article is part 6 (and the final part) of the Introduction to knowledge graphs series of articles.

Recent research1 has identified the development of knowledge graphs as an important aspect of artificial intelligence (AI) in knowledge management (KM). The same research also recommended the training of knowledge scientists who can build knowledge graphs that represent background knowledge and that complement training data.

To assist in advancing AI in KM, this 6-part series of articles has provided an introduction to knowledge graphs.

Part 1 defines knowledge graphs and summarizes their applications. Two definitions are put forward – one general, and the other technical. The “knowledge” in the term “knowledge graphs” refers to what Nonaka and Takeuchi call “explicit knowledge,” that is, something that is known and can be written down. The recent applications of knowledge graphs include organizing knowledge over the internet, data integration in enterprises, and artificial intelligence.

Part 2 charts the history of knowledge graphs, an awareness of which is considered very important.

Parts 3, 4, and 5 then draw on Hogan and colleagues’ comprehensive tutorial article2 and other research to provide an introduction to the technical aspects of knowledge graphs. To keep the discussion accessible, Hogan and colleagues’ present concrete examples for a hypothetical knowledge graph, which are reproduced in parts 3, 4, and 5. This hypothetical knowledge graph relates to tourism in Chile.

Part 3 outlines graph data models and the languages used to query and validate them.

Part 4 present deductive formalisms by which knowledge can be represented and entailed.

Part 5 describes inductive techniques by which additional knowledge can be extracted.

Knowledge graphs serve as a common substrate of knowledge within an organisation or community, enabling the representation, accumulation, curation, and dissemination of knowledge over time. In this role, knowledge graphs have been applied for diverse use-cases, ranging from commercial applications – involving semantic search, user recommendations, conversational agents, targeted advertising, transport automation, and so on – to open knowledge graphs made available for the public good.

General trends include the use of knowledge graphs to integrate and leverage data from diverse sources at large scale, and the combination of deductive (rules, ontologies, etc.) and inductive techniques (machine learning, analytics, etc.) to represent and accumulate knowledge.

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

References:

  1. Jarrahi, M. H., Askay, D., Eshraghi, A., & Smith, P. (2023). Artificial intelligence and knowledge management: A partnership between human and AI. Business Horizons, 66(1), 87-99.
  2. 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 a knowledge management (KM), environmental management, and education thought leader with more than 40 years of experience. As editor and lead writer of the award-winning RealKM Magazine, he has personally written more than 500 articles and published more than 2,000 articles overall, resulting in more than 2 million reader views. With a demonstrated ability to identify and implement innovative solutions to social and ecological complexity, Bruce has successfully completed more than 40 programs, projects, and initiatives including leading complex major programs. His many other career highlights include: leading the KM community KM and Sustainable Development Goals (SDGs) initiative, using agile approaches to oversee the on time and under budget implementation of an award-winning $77.4 million recovery program for one of Australia's most iconic river systems, leading a knowledge strategy process for Australia’s 56 natural resource management (NRM) regional organisations, pioneering collaborative learning and governance approaches to empower communities to sustainably manage landscapes and catchments in the face of complexity, being one of the first to join a new landmark aviation complexity initiative, initiating and teaching two new knowledge management subjects at Shanxi University in China, and writing numerous notable environmental strategies, reports, and other works. Bruce is currently a PhD candidate in the Knowledge, Technology and Innovation Group at Wageningen University and Research, and holds a Master of Environmental Management with Distinction and a Certificate of Technology (Electronics). As well as his work for RealKM Magazine, Bruce currently also teaches in the Beijing Foreign Studies University (BFSU) Certified High-school Pathway (CHP) program in Baotou, Inner Mongolia, China.

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