C. De Maio

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.

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.

OWL-FC: an upper ontology for semantic modeling of Fuzzy Control

This work introduces an OWL-based upper ontology, called OWL-FC (Ontology Web Language for Fuzzy Control), capable to support a semantic definition of Fuzzy Control. It focuses on the fuzzy rules representation by providing domain independent ontology, supporting interoperability and favoring domain ontologies re-usability. The main contribution is that OWL-FC exploits Fuzzy Logic in OWL to model vagueness and uncertainty of the real world. Moreover, OWL-FC enables automatic discovery and execution of fuzzy controllers, by means of context aware parameter setting: appropriate controllers can be activated, depending on the parameters proactively identified in the work environment. In fact, the semantic modeling of concepts allows the characterization of constraints and restrictions for the identification of the right matches between concepts and individuals. OWL-FC ontology provides a wide, semantic-based interoperability among different domain ontologies, through the specification of fuzzy concepts, independently by the application domain. Then, OWL-FC is coherent to the Semantic Web infrastructure and avoids inconsistencies in the ontology.

Hybrid methodologies to foster ontology-based knowledge management platform

Nowadays, a multitude of users benefits from social interactions, blogging, wiki in order to share their own contents with each other (i.e., user-generated content). In fact, both Web 2.0 and Enterprise 2.0 applications have changed the knowledge sharing paradigm, and have introduced enabling features to foster information flow among users. Nevertheless, the availability of large amount of information targeted to human employment highlights reusing, reasoning and exploitation of available knowledge. Emerging Semantic Web technologies enable to codify information in a machine understandable way. Therefore, the latest web development trend is devoted to combine Web 2.0 features with semantic technologies (e.g. semantic tagging, semantic wiki). This scenario raises new requirements in terms of knowledge base extraction, update and maintenance. To this end, this work defines an ontology-based knowledge management platform that integrates methodologies aimed at supporting the life cycle of large and heterogeneous enterprises knowledge bases. In particular, the defined architecture relies on hybrid methodologies which apply computational intelligence techniques and Semantic Web technologies to support Knowledge Extraction, Ontology Matching and Ontology Merging.

Swarm-based semantic fuzzy reasoning for situation awareness computing

Situation awareness computing employs sensor networks to collect large amounts of heterogeneous data in different and complex environments. The rapid development and deployment of sensor technology stress the problem related to the availability of too much and heterogeneous data. Last trend emphasizes the semantic annotation of acquired sensor data. Semantic sensor data provides machine understandable contextual information. In particular, the availability of semantic sensor data allows situation awareness in several application domains. This paper introduces a swarm-based approach to semantic web reasoning in order to identify situations. On one hand, fuzzy control has been employed in order to face with uncertainty of happening situations. On the other hand, Situation Theory has been used in order to model situation awareness. A multi agent swarm architecture enables to monitor complex environments by using spatially distributed autonomous sensors. An application scenario for bank intrusion detection has been described.

Swarm-based approach to evaluate fuzzy classification of semantic sensor data

Sensor networks currently are employed to collect large amounts of heterogeneous data in different and wide environments. Nevertheless, the rapid development and deployment of sensor technology stress the problem related to the availability of too much data and not enough knowledge. Last trend emphasizes the semantic annotation of sensor data. Semantic sensor data increase interoperability between heterogeneous sensor networks and provide contextual information to support situation awareness and management in several application domains. This work defines a framework aimed to reason on distributed semantic sensor data. In particular, defined approach combines swarm intelligence and Fuzzy Control theory in order to infer emerging situation by performing fuzzy classification of semantic sensor data. Swarm architecture enables us to monitor environment by using spatially distributed autonomous sensors. Fuzzy Control theory allows managing of the uncertainty of data sensing. An application scenario for broadcasting traffic news has been described.

f-SPARQL extension and application to support context recognition

Context aware computing as well as wearable and ubiquitous computing often attain with pattern recognition on incoming sensor data. Recognizing more (useful) contexts requires more information about the context, and thus more sensors and better recognition algorithms. In order to enable logic inference on incoming data, the proposed work assumes that incoming data are represented by means of semantic languages (e.g., RDF, OWL, etc.). Nevertheless, in a context aware computing purely logic-based reasoning on context may not be enough. So, the work introduces soft computing techniques to approximate context recognition. Specifically, this paper introduces an approach to context analysis and recognition that relies on f-SPARQL[1] tool, that is a flexible extension of SPARQL. In particular, in this work a JAVA implementation of f-SPARQL and the integrated support for fuzzy clustering and classification are discussed. This tool is exploited in the architecture that foresees some task oriented agents in order to achieve context analysis and recognition in order to identify critical situations. Finally, a simple application scenario and preliminary experimental results have been described.

RSS-Generated Contents through Personalizing e-Learning Agents

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 learners and teachers viewpoints, enabling them to become aware of new blog posts in educational blogging scenarios, to keep track of new shared media, etc. This paper presents an approach to enrich personalized e-learning experiences with user-generated content, through the RSS-feeds fruition. The synergic exploitation of Knowledge Modeling and Formal Concept Analysis techniques enables the definition and design of a system for supporting learners in the didactic activities. An agent-based layer supervises the extraction and filtering of RSS feeds whose topics are specific of a given educational domain. Then, during the execution of a specific learning path, the agents suggest the most appropriate feeds with respect to the subjects in which the students are currently engaged in.

Ontology-based knowledge structuring: an application on RSS Feeds

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.

What-if analysis combining Fuzzy Cognitive Map and Structural Equation Modeling

Nowadays, public funded universities have to increase their traction in acquiring students and in gaining and retaining their commitment, satisfaction and loyalty. In the area of information management, the Structural Equation Modeling (SEM) technique has been widely applied to perform inter-construct causal analysis (i.e. Commitment, Quality of Services, Loyalty, etc.) on specific observed indicators in order to model and analyze student loyalty in educational institutions. Nevertheless, SEM does not support what-if analysis contrived to consider decision-making scenarios, such as: what happens to student loyalty if managers adopt a quality-based strategy, and so on. This work proposes to combine SEM results with a Fuzzy Cognitive Map (FCM) to support what-if analysis and to determine the best strategy to adopt for the case study of the Relationship Quality-Based Student Loyalty Model (RQSL). Specifically, the causal models retrieved from SEM will be exploited as the input map of concepts enabling FCM. Subsequently, FCM is performed considering different input configurations corresponding to specific managing strategies (e.g. investing in quality of service, or quality of teaching, etc.) activating/deactivating various input concepts in order to simulate implications on the output constructs (e.g., Loyalty, Commitment, etc.). The results of the experiments provide reasonably good estimates of the impact on student loyalty deriving from investing in each specific factor and provides a helpful analysis that will support decision making. Furthermore, this study highlights the opportunities deriving from the cross-fertilization between the management and computer science domains.