Giuseppe Fenza

Hierarchical web resources retrieval by exploiting Fuzzy Formal Concept Analysis

In recent years, knowledge structuring is assuming important roles in several real world applications such as decision support, cooperative problem solving, e-commerce, Semantic Web and, even in planning systems. Ontologies play an important role in supporting automated processes to access information and are at the core of new strategies for the development of knowledge-based systems. Yet, developing an ontology is a time-consuming task which often needs an accurate domain expertise to tackle structural and logical difficulties in the definition of concepts as well as conceivable relationships. This work presents an ontology-based retrieval approach, that supports data organization and visualization and provides a friendly navigation model. It exploits the fuzzy extension of the Formal Concept Analysis theory to elicit conceptualizations from datasets and generate a hierarchy-based representation of extracted knowledge. An intuitive graphical interface provides a multi-facets view of the built ontology. Through a transparent query-based retrieval, final users navigate across concepts, relations and population.

Hybrid approach for context-aware service discovery in healthcare domain

Context-awareness computing is a research field which often refers to healthcare as an interesting and rich area of application. Context aware computing attains environments monitoring by means of sensors to provide relevant information or services according to the identified context. In particular, wireless ad hoc sensor networks for medical purposes are playing an increasing role within healthcare. Body Sensor Networks (BSN) are being designed for prophylactic and follow-up monitoring of patients in e.g. their homes, during hospitalization, and in emergencies. This work presents an integrated environment aimed at providing personalized healthcare services which appropriately meet the user@?s context. Deploying the semantics embedded in web services and context models is a mandatory step in the automation of service discovery, invocation and composition. Nevertheless, in a context aware domain purely logic-based reasoning on respectively context and services may not be enough. The main idea of this work is related to enrich with qualitative representation of context underling data by means of Fuzzy Logic in order to automatically recognize the context and to consequently find the right set of healthcare services among the available ones. Semantic formalisms (e.g., OWL, OWL-S, etc.) enable the context and services modeling in terms of domain ontology concepts. On the other hand, soft computing techniques support activity of unsupervised context analysis and healthcare semantic service discovery. Goal is to define context-aware system whose quality of retrieved services relies on the acquisition of user context by means of a robust theoretical approach. Moreover, this work defines hybrid architecture which attains a synergy between the agent-based paradigm and the fuzzy modeling. Specifically, the system exploits some task oriented agents in order to achieve context recognition, services matchmaking and brokerage activities.

A knowledge-based framework for emergency DSS

Emergency management requires a shared vision on everything that happens nearby the emergency zone and on the availability of resources enabling to face emergency situations. Specifically, emergency managers need to be concretely supported, by knowledge-based systems, to make critical decisions. This work introduces a framework that exploits Semantic Web technologies to harmonize heterogeneous data and soft computing methods in order to handle uncertainties and to model causal inference embedded into an emergency plan. In particular, the paper presents an approach based on Fuzzy Cognitive Maps (FCMs) to support knowledge processing and resources discovery according to the emergency features.

Towards an automatic fuzzy ontology generation

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.

RSS-based e-learning recommendations exploiting fuzzy FCA for Knowledge Modeling

Nowadays, Web 2.0 focuses on user generated content, data sharing and collaboration activities. Formats like Really Simple Syndication (RSS) provide structured Web information, display changes in summary form and stay updated about news headlines of interest. This trend has also affected the e-learning domain, where RSS feeds demand for dynamic learning activities, enabling learners and teachers to access to new blog posts, to keep track of new shared media, to consult Learning Objects which meet their needs. This paper presents an approach to enrich personalized e-learning experiences with user-generated content, through a contextualized RSS-feeds fruition. The synergic exploitation of Knowledge Modeling and Formal Concept Analysis techniques enables the design and development of a system that supports learners in their learning activities by collecting, conceptualizing, classifying and providing updated information on specific topics coming from relevant information sources. An agent-based layer supervises the extraction and filtering of RSS feeds whose topics cover a specific educational domain.

Friendly web services selection exploiting fuzzy formal concept analysis

This work describes a system for supporting the user in the discovery of semantic web services, taking into account personal requirements and preference. Goal is to model an ad-hoc service request by selecting conceptual terms rather than using strict syntax formats. Through a concept-based navigation mechanism indeed, the user discovers conceptual terminology associated to the web resources and uses it to generate an appropriate service request which syntactical matches the names of input/output specifications. The approach exploits the fuzzy formal concept analysis for modeling concepts and relative relationships elicited from web resources. After the request formulation and submission, the system returns the list of semantic web services that match the user query.

Agent-based Cognitive approach to Airport Security Situation Awareness

Situation awareness is crucial factor in decision-making. It involves monitoring and identification of relationships among objects in collaborative dynamic environments. In the domain of Airport Security one of the main needs is to support the security operator to manage in real-time risk scenarios in the airside. This work relies on a cognitive approach to model the awareness ontology and introduces an agent-based architecture to address the problem. In particular, in order to model situation awareness the work instantiates the generic Situation Theory Ontology(STO) in the specific domain of airport security. Furthermore, some task-oriented agents allow to distribute the information in order to achieve better performances.

Unfolding social content evolution along time and semantics

Abstract In the context of social media, the unstructured and dynamic nature of exchanged data and the information overload contribute to the growth of the number of research works proposing methods to improve performance of intelligent analytics services considering both time and semantics of the shared content. The presented paper focuses on the definition of a knowledge tracking framework to answer questions, such as “What is the semantic evolution of a topic (or news) along the time?”, “How did we arrive to a specific event?”, “What is the evolution of the topics of interest of a user?”, and so on. Our interest is about the elicitation of temporal patterns revealing the evolution of concepts along the time from a social media data stream; we focus on Twitter. Such patterns can be extracted at different levels of abstraction by considering different-sized time intervals and different scopes driven by the conceptualization of users’ queries. To address the proposed aim, we extend Temporal Concept Analysis and we use Description Logic to reason on semantically represented tweet streams. The evaluation activity reveals promising results from both sides quantitative and qualitative.

A framework for context-aware heterogeneous group decision making in business processes

In Business Process Management great attention is given to Computational Intelligence for supporting process life-cycle. Several approaches have been defined to support human decision making. The main drawback is that there are no solid criteria for determining optimal decisions since context, matter of discussion, and involved actors may differ at each execution. This work focuses on the definition of a framework to support and trace human decision making activities, in business processes, when heterogeneous decision-makers have to find a consensus to select most promising alternative to follow. The framework relies on Fuzzy Consensus Model and implements Reinforcement Learning algorithm to learn weight of the decision-makers through the analysis of past process executions considering context and performances of business processes. Context awareness relies on semantic web technologies enabling ontological reasoning to evaluate context similarity used to assign the right weight to the involved decision-makers also in the case when more general or more specific context occurs. The framework has been instantiated in the case study of Supply Chain Management. The analysis of the simulation results reveal that the proposed weight learning algorithm and the considered initial weight association strategies (Starting Weight and Training Executions), even if the cold start, give to decision-makers the chance to fill the gap with respect to more experienced decision makers.

A hybrid context aware system for tourist guidance based on collaborative filtering

In the area of ambient intelligence there is a need to address user needs according with context features. Recently, the synergy between context aware computing and collaborative filtering is leading to enhance recommender systems with capabilities always nearer to user needs. Specifically, in the domain of tourism it is useful to proactively suggest right sets of attractive locations, events and so on. This work defines a context aware recommender system aimed at suggesting pertinent points of interest (POIs) to tourists. In particular, the approach is strongly based on the synergy between soft computing and data mining techniques. The general framework integrates user profiles, history of social networking and POIs data. Then by defining collaborative filtering approach on the history meaningful POIs are extracted. Indeed, soft computing techniques are mainly applied in order to support activity of unsupervised users and POIs classification. On the other hand, data mining techniques are exploited in order to extract rules able to associate user profile and context features with an eligible set of recommendable POIs. Experimental results show performance in terms of recommendations accuracy.