An Entropy-Based Evaluation Method For Knowledge Bases Of Medical Information Systems

Keywords

Information entropy; Knowledge base evaluation; Knowledge representation

Abstract

In this paper we introduce a method to develop knowledge bases for medical decision support systems, with a focus on evaluating such knowledge bases. Departing from earlier efforts with concept maps, we developed an ontological-semantic knowledge base and evaluated its information content using the metrics we have developed, and then compared the results to the UMLS backbone knowledge base. The evaluation method developed uses information entropy of concepts, but in contrast to previous approaches normalizes it against the number of relations to evaluate the information density of knowledge bases of varying sizes. A detailed description of the knowledge base development and evaluation is discussed using the underlying algorithms, and the results of experimentation of the methods are explained. The main evaluation results show that the normalized metric provides a balanced method for assessment and that our knowledge base is strong, despite having fewer relationships, is more information-dense, and hence more useful. The key contributions in the area of developing expert systems detailed in this paper include: (a) introduction of a normalized entropy-based evaluation technique to evaluate knowledge bases using graph theory, (b) results of the experimentation of the use of this technique on existing knowledge bases.

Publication Date

3-15-2016

Publication Title

Expert Systems with Applications

Volume

46

Number of Pages

262-273

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.eswa.2015.10.023

Socpus ID

84946780967 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84946780967

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