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Updated: Mar 9, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
Published on: July 22, 2025
Modelling expertise at different levels of granularity using semantic similarity measures in the context of
Hasti Ziaimatin1, Tudor Groza1, Tania Tudorache2
1School of ITEE, The University of Queensland, Queensland, Australia.
Abstract:
Collaboration platforms provide a dynamic environment where the content is subject to ongoing evolution through expert contributions. The knowledge embedded in such platforms is not static as it evolves through incremental refinements - or micro-contributions. Such refinements provide vast resources of tacit knowledge and experience. In our previous work, we proposed and evaluated a Semantic and Time-dependent Expertise Profiling (STEP) approach for capturing expertise from micro-contributions. In this paper we extend our investigation to structured micro-contributions that emerge from an ontology engineering environment, such as the one built for developing the International Classification of Diseases (ICD) revision 11. We take advantage of the semantically related nature of these structured micro-contributions to showcase two major aspects: (i) a novel semantic similarity metric, in addition to an approach for creating bottom-up baseline expertise profiles using expertise centroids; and (ii) the application of STEP in this new environment combined with the use of the same semantic similarity measure to both compare STEP against baseline profiles, as well as to investigate the coverage of these baseline profiles by STEP.
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