#5022. Intelligent knowledge consolidation: From data to wisdom[Formula presented]

July 2026publication date
Proposal available till 24-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Management Information Systems;
Information Systems and Management;
Software;
Artificial Intelligence;
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Abstract:
Knowledge based systems have accomplished remarkable achievements in assisting evidence based decision making for complex problems. Most of the existing literature utilized a single modal, while very few have combined multi-modalities (mainly two) for knowledge acquisition. This paper presents the research work, driving the realization of such a comprehensive framework, in the field of healthcare. Using area specific, state-of-the-art machine learning techniques, we first extract knowledge from structured and unstructured data, which is consolidated with expert knowledge and managed through ripple down rules. Our presented technique shows an accuracy of 92.05%, which is much higher than single modal deep learning at 78.20%, naive bayes at 69.70%, logistic regression at 61.20%, expert driven knowledge at 86.02%, and naive knowledge combination at 70.86%. We provide the foundations for an accurate and evolvable knowledge-base, that can greatly enhance decision making in the healthcare domain.
Keywords:
Incremental knowledge model; Knowledge based system; Knowledge consolidation; Ripple down rules

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