Carmen De Maio

The current “semantic” generation of Web strongly lies in sharing knowledge rather than linkages among digital resources. The Semantic Web represents an effective infrastructure based on ontologies, languages and tools to enhance visibility of knowledge on the net. The imprecise nature of knowledge often requires fuzzy techniques to coherently represent the imprecise and uncertain information of the real world. It is indubitable that many decision making problems within business, industrial and web applications are solved by fuzzy approaches, especially by exploiting fuzzy control. In order to integrate fuzzy knowledge in the Semantic Web, appropriate formal schemas are introduced for describing fuzzy data types and uncertainty information. In particular, this paper presents an OWL-based upper ontology, called OWL-FC (Ontology Web Language for Fuzzy Control) which provides a set of ontological constructs for defining semantic specification of Fuzzy Control. The OWL-FC ontology represents a straightforward contribute to support automation in discovery, usage and interoperability among a large number of fuzzy controls; built-in markups for the fuzzy controls enable the natural integration in description logics-based reasoners and guarantee the specification of fuzzy concepts which do not depend on the application domain.

This paper presents a framework for automatically structuring knowledge, in order to reveal the intrinsic relationships among data. The approach exploits a fuzzy extension of Formal Concept Analysis theory, applied to RSS feeds. On the basis of the RSS Feed content, fuzzy FCA generates an ontology oriented knowledge network, enabling the accessibility of web resources presented by feeds. The framework achieves a semantic aggregation of feeds through the analysis of the feed content.

The huge growth of data on the Web and the requirement of semantic content analysis make the knowledge management and data mining very difficult activities. The knowledge elicitation, codification, and storage need not trivial techniques to improve formal information structuring on the Internet. Ontologies provide conceptualization and processing knowledge, sharing of consolidate understanding, reusing of domain knowledge codification for many Web applications. Manual construction of a domain-specific ontology is an intensive and time-consuming process, which requires an accurate domain expertise, because of structural and logical difficulties in the definition of concepts, as well as conceivable relationships. At the same time, the ontology visualization process requires similar endeavors to support ontology management, exploration, and browsing. This work describes an automatic method for ontology design from the content analysis of Web resources. The approach exploits a fuzzy extension of formal concept analysis model for structuring the elicited knowledge, viz. concepts and relations embedded in the resources content. Final result is an effective ontology visualization through a navigable, facet-based view of the built ontology across the extracted concepts and their own population. Furthermore, the approach proposes a simple labeling of ontology concepts through a sketched and intuitive process. © 2010 Wiley Periodicals, Inc.

Nowadays, the emphasis on Web 2.0 is specially focused on user generated content, data sharing and collaboration activities. Protocols like RSS (Really Simple Syndication) allow users to get structured web information in a simple way, display changes in summary form and stay updated about news headlines of interest. In the e-Learning domain, RSS feeds meet demand for didactic activities from

In situations where emergencies have to be handled, it’s vital to provide a vision, shared by all involved actors, on everything happens near the geographic area interested by the emergency and on the availability of resources like hospitals, ambulances, fire fighters, volunteers and so on. This work introduces an approach for the smart discovery of geo-located resources (i.e. people, services, etc.) with respect to specific emergency requirements. The proposed approach is strongly based on the semantic modeling of geo-spatial data, geo-localized services, people’s skills and geo-positions and on a novel method exploiting Fuzzy Cognitive Maps (FCMs) in order to suitably elicit resource plans according to the occurred event. Semantic modeling of resources highlights value added in term of support to human resources managers. In the next future the discovery performance will be tested in real conditions.

Applying best available evidences to clinical decision making requires medical research sharing and (re)using. Recently, computer assisted medical decision making is taking advantage of Semantic Web technologies. In particular, the power of ontologies allows to share medical research and to provide suitable support to the physician's practices. This paper describes a system, named ODINO (Ontological Disease kNOwledge), aimed at supporting medical decision making through semantic based modeling of medical knowledge base. The system defines an ontology model able to represent relations between medical disease and its symptomatology in a qualitative manner by using fuzzy labels. Medical knowledge is defined according with physician experts members of INMP (National Institute for Health Migration and Poverty). The main aim of ODINO is to provide an effective user interface by using ontologies and controlled vocabularies and by allowing faceted search of diseases. In particular, this work mashes the capabilities of Description Logic reasoners and information retrieval techniques in order to answer to physician's requests. Some experimental results are given in the field of dermatological diseases.

Nowadays, in enterprise environments there is a wide and consolidated utilization of software for the human resource management providing functionalities like organizational management, personnel development, training event management, etc. that lay upon a competencies repository mostly populated through expensive and inefficient data entry activities. The new trends in Web 2.0 see a paradigm namely Enterprise 2.0, for supporting business activities

In recent years, the success of Semantic Web is strongly related to the diffusion of numerous distributed ontologies enabling shared machine readable contents. Ontologies vary in size, semantic, application domain, but often do not foresee the representation and manipulation of uncertain information. Here we describe an approach for automatic fuzzy ontology elicitation by the analysis of web resources collection. The approach exploits a fuzzy extension of Formal Concept Analysis theory and defines a methodological process to generate an OWL-based representation of concepts, properties and individuals. A simple case study in the Web domain validates the applicability and the flexibility of this approach.

This work proposes a system process for extracting automatically a fuzzy ontology from a collection of web resources. The approach exploits the Formal Concept Analysis theory for structuring the elicited knowledge, viz. concepts and relations embedded in the resources information. A simple graphical interface provides a multi-facets view which allows final users to navigate across the concepts and the relative population.