A novel family of IC-based similarity measures with a detailed experimental survey on WordNet

Measure of Semantic Likeness Among Business Process Activities in a Telecommunication Company

“…Business models provided by the telecommunications company are used for comparison of activities. In total ten models were used ( Figures 6 to 15 attached at appendix), three of them are reference models (1,4,7); each one was evaluated with the total set of models, obtaining a similarity ranking for each evaluated process. Process repository models are: model 1 -recruitment of operational staff; model 2 -recruitment of administrative staff; model 3 -recruitment process; model 4 -resource request process; model 5 -resource entry process; model 6 -customer invoicing process; model 7tender process; model 8 -work strategy process; model 9initial production process and model 10 -model of production of electrical cabinet.…”

“…Authors such as [25] discuss the comparison of the business process models by integrating two measures explained above, the first measure includes obtaining a similarity between the nodes comparing the label of the activities and the connectors that the business process may have, and then to use the methodology of the graphic distance proposed in [24], then performs a combination of both measures to obtain a single measure of similarity (4).…”

“…Currently the use of optimization tools [1], [2], process description and artificial intelligence [3] enable an understanding of the objectives of the business at all levels of the organization, approaching a number of complex problems [4]. Computational mathematical modeling applied to calculation of semantic similarity is considered an important method in many areas of research [5].…”

Weighting-based semantic similarity measure based on topological parameters in semantic taxonomy

“…Taxonomical parameters have been used in path-based measures (Al-Mubaid and Nguyen 2006; Wu et al 2006; Wang and Hirst 2011) to handle the specificity issue and in intrinsic IC-based measures (Seco, Veale and Hayes 2004; Zhou, Wang and Gu 2008; Pirró 2009; Sánchez, Batet and Isern 2011; Taieb et al 2011; Meng, Gu and Zhou 2012; Sánchez and Batet 2012; Yuan, Yu and Wang 2013; Harispe et al 2014; Lastra-Díaz and García-Serrano 2015a; Lastra-Díaz and García-Serrano 2015b; Jiang et al 2016) to address the issue of dependency on external knowledge sources (tagged corpora) for modeling the IC of concepts. The experimental results showed that these taxonomical parameters are useful in handling issues on measuring semantic similarity.…”

“…Although the features of these measures are hierarchically extracted to represent the meaning of a concept, the topological (structural) parameters, such as the depth and number of descendants, that reflect the specificity or generality of the concepts are ignored. These topological parameters are important in measuring semantic similarity and have been widely used to address specificity in path-based and information content (IC)-based measures (Lastra-Díaz and García-Serrano 2015b). Consequently, ignoring these topological parameters in the hierarchical feature-based measure may affect the performance of this measure.…”