Robust Selection of Domain-Specific Semantic Similarity Measures from Uncertain Expertise

Abstract

Knowledge-based semantic measures are cornerstone to exploit ontologies not only for exact inferences or retrieval processes, but also for data analyses and inexact searches. Abstract theoretical frameworks have recently been proposed in order to study the large diversity of measures available; they demonstrate that groups of measures are particular instantiations of general parameterized functions. In this paper, we study how such frameworks can be used to support the selection/design of measures. Based on (i) a theoretical framework unifying the measures, (ii) a software solution implementing this framework and (iii) a domain-specific benchmark, we define a semi-supervised learning technique to distinguish best measures for a concrete application. Next, considering uncertainty in both experts' judgments and measures' selection process, we extend this proposal for robust selection of semantic measures that best resists to these uncertainties. We illustrate our approach through a real use case in the biomedical domain. © Springer International Publishing Switzerland 2014.