Special issue on fuzzy ontologies and fuzzy markup language applications

Latest research on formal methods for knowledge representation based on semantic web technologies witnessed how ontologies have become a fundamental and critical component for developing applications in different real world scenarios. Indeed, ontologies formally model application domains to enable reuse of knowledge and allow people and software agents to share a common understanding of the structure of information. The body of a knowledge modeled through an ontological approach is based on the conceptualization idea, i.e., an abstract and simplified view of the knowledge related to given application scenario. However, it is widely pointed out that classical ontology model is not sufficient to deal with imprecise and vague knowledge strongly characterizing some real world applications. Soft computing techniques can enhance ontological knowledge representation by providing methods for directly dealing with imprecision and vagueness. Indeed, fuzzy set theory may extend the conventional ontology idea with a collection of fuzzy or vague terms like ‘‘creamy’’, ‘‘hot’’, ‘‘large’’, for which a clear and precise definition is not possible. In this scenario, the fuzzy markup language (FML) represents an important result because it allows fuzzy scientists to express their ‘‘imprecise’’ ideas in abstract and interoperable way by improving their productivity and, at the same time, increasing the average quality of their works. The objective of this special issue is to highlight an ongoing research on fuzzy and FML approaches for knowledge semantic representation based on ontologies, as well as their applications on various domains. This volume contains seven papers that consider different aspects of research on fuzzy ontologies and FML. The first four papers describe the applications exploiting fuzzy theory and ontology. Updating Generalized Association Rules with Evolving Fuzzy Taxonomies by WenYang Lin, Ja-Hwung Su, and Ming-Cheng Tseng proposes an algorithm for mining generalized association rules with fuzzy taxonomic structures where the taxonomy may change as time passes; as shown by their empirical evaluations, the proposed algorithms yield high performances even in high degree of taxonomy evolution. Crowdsourcing techniques to create a fuzzy subset of SNOMED CT for semantic tagging of medical documents by David T. Parry and Tsung-Chun Tsai describes an approach to identifying subsets of medical knowledge contained in the SNOMED CT dictionary via a crowdsourcing technique. The research aims to assist clinicians in coding small freetext documents such as radiology reports. Fuzzy Ontologies-based User Profiles applied to enhance e-Learning Activities by Mateus Ferreira-Satler, Francisco P. Romero, Victor H. Menendez-Dominguez, Alfredo Zapata and Manuel E. Prieto shows how a fuzzy ontology can be used to represent user profiles into a recommender engine and enhances the user’s activities into e-Learning environments. Decision making with a fuzzy ontology by Christer Carlsson, Matteo Brunelli and Jòzsef Mezei shows how soft computing techniques, e.g. aggregation functions and interval valued fuzzy numbers, will support effective and practical decision making on the basis of the fuzzy G. Acampora (&) Department of Computer Science, University of Salerno, Salerno, Italy e-mail: [email protected]