Abstract
We present new metrics and techniques which allow one to configure a metadata catalogue and objectively describe knowledge management ontologies. Per C.E. Shannon (1948), when describing information based systems, statistical measures are a necessity; yet very few ontology based standards mention quantifiable measures such as entropy, data encapsulation, complexity, efficiency, evolution, or redundancy. We hope to demonstrate how statistical information measures can be implemented for ontology-based knowledge management systems using our \(\mathbb{L}_0 \) statistic, entropy, evolution, organization, sensitivity, and an interpretation of complexity.
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© 2005 International Federation for Information Processing
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Pefferly, R.J., Jaeger, M.C., Lo, M. (2005). Metrics for Objective Ontology Evaluations. In: Bramer, M., Terziyan, V. (eds) Industrial Applications of Semantic Web. IASW 2005. IFIP — The International Federation for Information Processing, vol 188. Springer, Boston, MA. https://doi.org/10.1007/0-387-29248-9_11
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DOI: https://doi.org/10.1007/0-387-29248-9_11
Publisher Name: Springer, Boston, MA
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