Record link: 
http://hdl.handle.net/20.500.12708/217252
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Title: 
Knowledge Engineering in the Era of Artificial Intelligence
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Citation: 
Hose, K. (2023). Knowledge Engineering in the Era of Artificial Intelligence. In A. Abelló, P. Vassiliadis, O. Romero, & R. Wrembel (Eds.), Advances in Databases and Information Systems : 27th European Conference, ADBIS 2023, Barcelona, Spain, September 4–7, 2023, Proceedings (pp. 3–15). Springer. https://doi.org/10.1007/978-3-031-42914-9_1
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Publisher DOI: 
10.1007/978-3-031-42914-9_1
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Language: 
English
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Authors: 
Hose, Katja 
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Organisational Unit: 
E192-02 - Forschungsbereich Databases and Artificial Intelligence 
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Published in: 
Advances in Databases and Information Systems : 27th European Conference, ADBIS 2023, Barcelona, Spain, September 4–7, 2023, Proceedings 
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ISBN: 
978-3-031-42913-2
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Volume: 
13985
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DOI of the book: 
10.1007/978-3-031-42914-9
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Date (published): 
2023
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Event name: 
27th European Conference on Advances in Databases and Information Systems 
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Event date: 
4-Sep-2023 - 7-Sep-2023
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Event place: 
Barcelona, Spain
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Number of Pages: 
13
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Publisher: 
Springer, Cham
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Peer reviewed: 
Yes
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Keywords: 
Knowledge Graphs; Graph Data Management; Graph Data Quality; Knowledge Engineering; Graph Data Models; Graph Data Querying
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Abstract: 
Knowledge engineering with respect to knowledge graphs and graph data in general is becoming a more and more essential component of intelligent systems. Such systems benefit from the wealth of structured knowledge, which does not only include native graph data, such as social networks or Linked Data on the Web, but also general knowledge describing particular topics of interest. Furthermore, the flexibility of the graph model and its ability to store data relationships explicitly enables the integration and exploitation of data from very diverse sources. Hence, to truly exploit their potential, it becomes crucial to provide intelligent systems with verifiable knowledge, reliable facts, patterns, and a deeper understanding of the underlying domains. This paper will therefore chart a number of current challenges in knowledge engineering and discuss opportunities.
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Research Areas: 
Logic and Computation: 20%
Information Systems Engineering: 80%
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Science Branch: 
1020 - Informatik: 80%
1010 - Mathematik: 20%
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Appears in Collections:
Conference Paper

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