End-user engineering of ontology-based knowledge bases

Knowledge bases store information on certain topics. Applying a well-structured and machine-readable format for a knowledge base is a prerequisite for any AI-based processing or reasoning. Semantic technologies (e.g. RDF) offer such a format and have the advantages that they make it possible to define the semantics of the information and support advanced querying. However, the disadvantage is that using such technologies is challenging for people not trained in IT, such as subject matter experts. This means that they need to rely on semantic technology experts to create, maintain, and query their knowledge bases. However, these experts are, in turn, not trained in the subject matter, while domain knowledge is essential for the construction of high-quality knowledge bases. In this paper, we present an end-user engineering approach for ontology-based knowledge bases. The goal is to allow subject matter experts to develop, maintain, and exploit the knowledge base themselves. We also present the supporting tools developed so far. The tools for the construction and the manual filling of the knowledge base are using the jigsaw metaphor to hide technicalities and guide the users. We also developed tools to automatically import data from spreadsheets into the knowledge base and to perform some type of quality control on the data. The end-user approach and the tools are demonstrated and evaluated for building a knowledge base in the toxicology domain.
Publication Reference
Audrey Sanctorum, Jonathan Riggio, Jan Maushagen, Sara Sepehri, Emma Arnesdotter, Mona Delagrange, Joery De Kock, Tamara Vanhaecke, Christophe Debruyne & Olga De Troyer (2022) End-user engineering of ontology-based knowledge bases, Behaviour & Information Technology, 41:9, 1811-1829, DOI: 10.1080/0144929X.2022.2092032

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