Decision support combining machine learning, knowledge representation and case-based reasoning

Show simple item record Mehli, Carlo Hinkelmann, Knut Jungling, Stephan 2022-02-14T06:41:41Z 2022-02-14T06:41:41Z 2021
dc.description.abstract Knowledge and knowledge work are essential for the success of companies nowadays. Decisions are based on knowledge and better knowledge leads to more informed decisions. Therefore, the management of knowledge and support of decision making has increasingly become a source of competitive advantage for organizations. The current research uses a design science research approach (DSR) with the aim to improve the decision making of a knowledge intensive process such as the student admission process, which is done manually until now. In the awareness phase of the DSR process, the case study research method is applied to analyze the decision making and the knowledge that is needed to derive the decisions. Based on the analysis of the application scenario, suitable methods to support decision making were identified. The resulting system design is based on a combination of Case-Based Reasoning (CBR) and Machine Learning (ML). The proposed system design and prototype has been validated using triangulation evaluation, to assess the impact of the proposed system on the application scenario. The evaluation revealed that the additional hints from CBR and ML can assist the deans of the study program to improve the knowledge management and increase the quality, transparency and consistency of the decision-making process in the student application process. Furthermore, the proposed approach fosters the exchange of knowledge among the different process participants involved and codifies previously tacit knowledge to some extent and provides relevant externalized knowledge to decision makers at the required moment. The designed prototype showcases how ML and CBR methodologies can be combined to support decision making in knowledge intensive processes and finally concludes with potential recommendations for future research. en_ZA
dc.description.department Informatics en_ZA
dc.description.librarian am2022 en_ZA
dc.description.uri en_ZA
dc.identifier.citation Mehli, C., Hinkelmann, K. & Jüngling, S. 2021, 'Decision support combining machine learning, knowledge representation and case-based reasoning', CEUR Workshop Proceedings, vol. 2846, pp. 1-12. en_ZA
dc.identifier.issn 1613-0073
dc.language.iso en en_ZA
dc.publisher CEUR Workshop Proceedings en_ZA
dc.rights © 2021 Copyright for this paper by its authors. This is an open access article published under the Creative Commons CC-BY-NC 3.0 license. en_ZA
dc.subject Machine learning en_ZA
dc.subject Decision support en_ZA
dc.subject Knowledge management en_ZA
dc.subject Knowledge representation en_ZA
dc.subject Knowledge-intensive process en_ZA
dc.subject Design science research (DSR) en_ZA
dc.subject Case-based reasoning (CBR) en_ZA
dc.title Decision support combining machine learning, knowledge representation and case-based reasoning en_ZA
dc.type Article en_ZA

Files in this item

This item appears in the following Collection(s)

Show simple item record