Narratives are essential tools through which politicians and public figures construct shared meanings and shape public perception, both locally and globally. This paper introduces a computational approach for systematically identifying and analyzing narrative structures in political speeches, aiming to enhance our understanding of how politicians try to convey their messages. A novel ontology, OntoNarr (Ontology for Narrative Representation), is defined and used to identify narrative schemas within the full text of the speeches. The core contribution is a more interpretable and conceptually coherent method for comparing political speeches based on their underlying narrative structures. This is achieved by converting ontology-based representations into graph embeddings and visualizing them using scatterplots. Unlike traditional NLP pipelines that rely primarily on lexical and syntactic features, this method incorporates a formal semantic structure, addressing key limitations in conventional analysis. A case study involving speeches from four politicians demonstrates how historical context influences the choice of narrative schema while also revealing some cross-temporal and cross-ideological similarities. Lastly, a method from granular computing is used to quantitatively evaluate the ontology-based approach.

In the evolving landscape of artificial intelligence, ensuring the robustness and explainability of machine learning models is valuable. This study presents an innovative method based on the Rough Set Theory and Principles of Justified Granularity to enhance the explainability of text-based classifiers, specifically in style-based news bias classification. The method helps understand why a classifier can be deceived with an Adversarial Attack. It leverages two levels of insight. The first level is independent of the specific classifier and consists of generating rules from a boundary region built with Rough Sets Theory starting from train data. The second level considers the behavior of a specific machine learning model in classifying manipulated observations and, starting from the classification results, constructs information granules of true positives and false negatives. These granules are representative of observations that deceived a classifier. By comparing boundary rules with information granules, it is possible to acquire actionable knowledge that is useful for making decisions on making a machine learning model more resilient. Results are evaluated with real data containing biased news. The success rate of adversarial examples generated using LLM to test classifiers on borderline cases, where minor textual changes cause false negatives, ranges from 45% to 68%.

This paper presents a novel approach for predicting and explaining instances of Information Disorder. The paper reports two significant findings: i) the use of structures of opposition to describe relationships between instances of Information Disorder, and ii) the development of an explainable prediction method that combines Fuzzy Rough Sets and TOPSIS with these structures. The findings have the potential to assist analysts and decision-makers in gaining a deeper understanding of the phenomenon of Information Disorder. The results are based on real data and demonstrate promising applications for future research.

The paper presents and evaluates an approach based on Rough Set Theory, and some variants and extensions of this theory, to analyze phenomena related to Information Disorder. The main concepts and constructs of Rough Set Theory, such as lower and upper approximations of a target set, indiscernibility and neighborhood binary relations, are used to model and reason on groups of social media users and sets of information that circulate in the social media. Information theoretic measures, such as roughness and entropy, are used to evaluate two concepts, Complexity and Milestone, that have been borrowed by system theory and contextualized for Information Disorder. The novelty of the results presented in this paper relates to the adoption of Rough Set Theory constructs and operators in this new and unexplored field of investigation and, specifically, to model key elements of Information Disorder, such as the message and the interpreters, and reason on the evolutionary dynamics of these elements. The added value of using these measures is an increase in the ability to interpret the effects of Information Disorder, due to the circulation of news, as the ratio between the cardinality of lower and upper approximations of a Rough Set, cardinality variations of parts, increase in their fragmentation or cohesion. Such improved interpretative ability can be beneficial to social media analysts and providers. Four algorithms based on Rough Set Theory and some variants or extensions are used to evaluate the results in a case study built with real data used to contrast disinformation for COVID-19. The achieved results allow to understand the superiority of the approaches based on Fuzzy Rough Sets for the interpretation of our phenomenon.

In online learning, the dropout phenomenon is a relevant issue to address with practical solutions. Several data sets stimulate original, and resolutive data analysis approaches, demonstrating the importance of the dropout phenomenon. This study proposes a novel approach to predicting massive online open course (MOOC) students at risk of dropout stressing the need to consider the temporal dimension in the data log. The proposal aims to build a data-driven decision support system able to identify students at risk of dropout based on the conceptualization of such students' behavior and its evolution along the time dimension. The primary theoretical model behind the proposed method is the formal concept analysis, and its temporal extension (i.e., temporal concept analysis) for analyzing timestamped data and carrying out a timed lattice. The main result of the paper is a method to extract behavioral patterns of MOOC students at risk of dropout. Such patterns are defined as Time-based Behavior Rules extracted from the aforementioned timed lattice obtained through the preprocessing of MOOC platform log files. The resulting rule set can be easily integrated for implementing educational DSS, as shown in the last part of the paper. The conducted experiments reveal promising results in terms of F-score and students' monitoring time.

This paper presents a comprehensive model for representing and reasoning on situations to support decision makers in Intelligence analysis activities. The main result presented in the paper stems from a work of refinement and abstraction of previous results of the authors related to the use of Situation Awareness and Granular Computing for the development of analysis methods and techniques to support Intelligence. This work made it possible to derive the characteristics of the model from previous case studies and applications with real data, and to link the reasoning techniques to concrete approaches used by intelligence analysts such as, for example, the Structured Analytic Techniques. The model allows to represent an operational situation according to three complementary perspectives: descriptive, relational and behavioral. These three perspectives are instantiated on the basis of the principles and methods of Granular Computing, mainly based on the theories of fuzzy and rough sets, and with the help of further structures such as graphs. As regards the reasoning on the situations thus represented, the paper presents four methods with related case studies and applications validated on real data.

This article presents a new interactive method to analyze and assess hypotheses, and its application to terrorism events. The method combines probability, fuzzy, and rough set theories and supports decision makers and analysts of counterterrorism in the analysis of intelligence information by using behavioral models of known terrorist groups. Starting from intelligence information about possible attack patterns, the proposed method uses two parameters allowing derivation and analysis of a wide range of hypotheses, and their assessment on the basis of different support levels of evidence. The evaluation of results has been done on real data relating to five years (2012-2016) of terrorist activities extracted from the Global Terrorism Database.

A great impetus for the study of resilience in critical infrastructures (CIs) is found in the large number of initiatives and international research programmes from U.S., EU, and Asia. Politicians, decision makers, and citizens are now aware of the drastic consequences that can have the cascading effects of an adverse event in these large scale infrastructures. However, the study of resilience in CIs is challenging for several reasons, among which their large scale and interdependencies. We have to consider also that adverse events, e.g., attacks, natural hazards, or man-made disasters, suddenly occur and evolve rapidly, giving us little time to take decisions and react to them. Approximate reasoning and rapid decision making have to be considered requirements for resilience analysis of CIs. The main result presented in this paper relates to a systemic integration of granular computing (GrC) and resilience analysis for CIs. Each phase of our approach presents distinctive aspects but, overall, we argue the merit of this paper consists in the originality of the study, being this the first work that combines GrC and resilience analysis of CIs. This paper reports an illustrative example that shows how to apply our results, and a discussion on the necessary contextualizations and extensions of the GrC results to be better adapted for CIs resilience.

With the emergence of new technology-supported learning environments (e.g., MOOCs, mobile edu games), efficient and effective tutoring mechanisms remain relevant beyond traditional intelligent tutoring systems. This paper provides an approach to build and adapt a tutoring model by using both artificial neural networks and reinforcement learning. The underlying idea is that tutoring rules can be, firstly, learned by observing human tutors' behavior and, then, adapted, at run-time, by observing how each learner reacts within a learning environment at different states of the learning process. The Zone of Proximal Development has been adopted as the underlying theory to evaluate efficacy and efficiency of the learning experience.

This work proposes the definition of a tool supporting the transition of children from kindergarten to primary school and, as a side effect, the development of problem solving and digital competences.The tool has been defined, by means of an ontology-driven approach, as an Intelligent Tutoring System (ITS) integrated to a structured game-based educational environment and provides benefits for both teachers and children.The definition of a novel ontology, namely ITSEGO, providing a model (generally applicable in different learning contexts) to build Game-based ITS and, the design of a concrete Game-based ITS for supporting the aforementioned transition are the main results of this work.

Nowadays, many research works are moving toward the definition of models for human decision support systems within business process executions. Existing solutions, in general, do not take into account the context in which such processes run but they provide rigid models that could erroneously support decision-making activities when a different context needs to be considered. This work focuses on the definition of a framework to support and trace human decision-making activities, in business processes, when more heterogeneous decision-makers have to find a consensus to select one alternative among the others. One class of such processes is that of Innovation Processes. In particular, the main result described here is a Context-aware Fuzzy Linguistic Consensus Model, based on Fuzzy Logic, Semantic Web technologies and Reinforcement Learning, that considers heterogeneous decision makers with different levels of influence (assigned by considering their past decisions) in the context where the decision activity takes place.

Situation identification is a complex task that is usually employed in order to sustain the work of Decision Support Systems in several and heterogeneous application scenarios like, for instance, Emergency Management, Safety and Security. Typically, situation awareness systems gather and process raw sensor data by means of different techniques. In this context, it is fundamental to exploit qualitative sensor data in order to guarantee the reliability of the situation identification task results. The consolidation of Internet of Things and the growth of the Linked Sensor Data ecosystem provide us with different degrees of availability and, sometimes, redundancy of sensor observations that could be conflicting. This could be caused by sensor failures due to contextual factors, malicious attacks, faults. This paper proposes an approach based on Fuzzy Consensus to assess data coming from a group of redundant sensors and provide reliable observations to be exploited for situation identification. Lastly, Granular Computing paradigm is adopted to handle multigranularity of information, i.e., To manage observations assessed in different linguistic term sets.

Workplace learning can be conceived as the set of processes related to learning and training activities at work. Typically, workplace learning includes formal, informal, and non-formal learning activities. Having a control on the whole learning process of each worker is a complex task. Indeed we have to align individual learning paths, real workers' needs (for instance in terms of tasks or projects to accomplish), career plans, and other organizational needs to activate virtuous knowledge flows. In order to accomplish this complex task a comprehensive framework is needed. This paper provides the definition of the aforementioned framework by exploiting semantic technologies in order to model (by means of Ontologies), represent, extract, and share knowledge within organizations. Although the proposed framework allows a wide range of workplace learning experiences, it mainly focuses on informal learning, on the ways it can be sustained by exploiting organizational resources, and on the capability of linking individual and organizational learning in the context of a widely accepted knowledge flow model like socialization, externalization, composition, and internalization.

Serendipitous Learning is the learning process occurring when hidden connections or analogies are unexpectedly discovered, mostly during searching processes (for instance on the Web) which are typical for informal learning activities, especially accomplished at the workplace context. Moreover, serendipitous processes have high probability to occur in the contexts where learners have high autonomy, more chances to intervene in different activities and to interact with resources and people. This paper proposes an approach based on the Social Semantic Web vision to sustain and improve Serendipitous Learning. The proposed approach considers two connected ontology layers to model knowledge by using several SemanticWeb vocabularies like SIOC, Dublin Core, SKOS, and so on. The SKOS role is particularly relevant because it allows connections among heterogeneous resources, also across multiple communities. The proposed approach models the above-mentioned connections at the conceptual level and facilitate learners in discovering them and following unexpected paths.

The concept of Seamless Learning is becoming more and more effective because the newer technologies are able to meet the personal needs of the people and really support them in their learning processes. Thus, the learning experience is a moment in the everyday life strongly related with the situation each person is dealing with. The main idea of this work is to define a flexible seamless learning environment able to identify the context where a learner is deepened in and to apply an adaptation by respecting her learning goals. The proposed approach leverages on three main aspects: situation awareness, adaptive learning and semantic technologies.

The wealth of modern enterprises has progressively shifted from tangible assets (capital, resources,) into intangible ones (knowledge, reputation, skills management, innovation processes,). Intangibles are closely related to the natural interactions normally occurring among work practices. This is where ideas, innovation, learning, knowledge, social cohesion, and other diverse intangibles synergistically contribute to performance, competition differentiators and value creation. In order to make these intangibles productive for the organization, we have to define suitable models for their correct representation in real contexts, as well as tools for their proper management in the workplace environment in a transparent way. All these aspects are the foundation of the ARISTOTELE research project. In this paper, we address two issues: i) the use of semantic technologies for modeling and cross relating relevant organizational assets, namely knowledge, competency, worker and learning, ii) how to design a software architecture for managing these models through the integration of ad-hoc developed tools and commercial off-the-shelf platforms.

Assessing employees' competences to properly support Competence-based Management processes (e.g. Career Development, Workforce Planning, etc.) in Organizations is a complex task. Difficulties concern with both the right assessment methodology and the most effective tools. Moreover, the assessment process is time-consuming both for assessors and assessees and often it is performed at the wrong time with considerable costs for external resources. This work proposes a novel approach, based on semantic technologies, to enhance competence assessment in Organizations by analysing content produced, tasks completed and professional relationships established by employees in their day by day activities at the workplace.

In an era in which organizations increasingly consider the competencies of their employees as a crucial resource, human management becomes a key activity for improving staff and business performances. Important is also knowing who knows what inside the organization, so that project teams are assembled as the right mix of skill, knowledge and workforce abilities. At the same time, trust, an essential component, related to understanding interpersonal and group behavior, is an indisputable prerequisite for organizational effectiveness, in terms of social-cognitive capital, global competency as well as economic exchange and social and political stability. This paper defines an approach to support competency-based management by providing recommendations about the reliability of a worker in terms of trust information and own competencies. The approach lies on an agent-based architecture which supervises the Human Resources Management (HRM). Task-oriented agents monitor the employees' profiles and capabilities by maintaining update the competencies and the trusts in the organizational social network. Particularly an agent endowed by fuzzy reasoning capabilities provides recommendation about workers' competencies in HRM decision-making processes.

Nowadays, the importance of knowledge management is well understood by managers in the organizations and, at the same time, the great significance of trust, in enabling effective knowledge sharing, is emerging. Presence or lack of trust can have serious implications for organizations with respect to the quality and of their business processes. Several scientific works have confirmed the direct correlation between social-cognitive capital, in terms of competencies and experiences, and a feeling of trust in both learning and working collaborative environments. On the other hand, Competency-Based Management allows organizations to link human resources processes to competencies in order to shape its workforce capabilities and to achieve better results. Typically, employees' competencies (and proficiency levels) are stored and used by specific enterprise software, whereas the trust is not considered or left to managers' feeling. This work proposes an approach to the improvement of collaborative learning activities by refining and allowing (at workers' level) competency-finding processes through the social calculus of trust-in-competencies degree.

Ontologies have been frequently employed in order to solve problems derived from the management of shared distributed knowledge and the efficient integration of information across different applications. However, the process of ontology building is still a lengthy and error-prone task. Therefore, a number of research studies to (semi-)automatically build ontologies from existing documents have been developed. In this paper, we present our approach to extract relevant ontology concepts and their relationships from a knowledge base of heterogeneous text documents. We also show the architecture of the implemented system and discuss the experiments in a real-world context.

The term Enterprise 2.0 applies to the use of Web 2.0 technologies as a support for business activities within the organizations. These technologies are exploited to foster inter-persons collaboration, information exchange and knowledge sharing, also outside the organization, to establish relationships based on conversational modalities rather than on traditional business communication. The vision of Enterprise 2.0 places a high value on the importance of social networks inside and outside the organization stimulating flexibility, adaptability and innovation between workers, managers, customers, suppliers and consultants. The integration between the Web 2.0 tools with traditional enterprise software, the aggregation of organization inner data with external data and the choice of adequate knowledge representations are critical aspects to be faced in order to further the growth of smart applications in the Enterprise 2.0 context. In this work we propose an approach, based on Semantic Web techniques, to relax the aforementioned critical issues.

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 within organizations. Web 2.0 is mainly exploited to sustain collaboration, information exchange and knowledge sharing. This work introduces an agent-based framework for the dynamic refinement of employees' competencies profiles by analyzing and monitoring collaborative activities executed through Enterprise 2.0 tools (e.g. corporate blogs, enterprise wikis, etc.). A fuzzy extension of Formal Concept Analysis model supports the elicitation of implicit knowledge and the content structuring into a conceptual representation. The resulting concept-based organization of initial user-generated content will be exploited to provide automatic hints to human resources (HR) managers in order to support them in making safer decisions that involve employees' competencies.

In an enterprise context, competencies are often dispersed across different teams. A specific need within the enterprise could not be satisfied only because of a lack of awareness about real competencies owned by employees. The aforementioned problem involves two main critical aspects: the difficulty to manage employees' competencies in order to constantly keep them up-to-date and the ability to agilely share employees' profiles across the organization in order to support competency finding. This work proposes an approach to relax the above critical points by integrating a semantic web-based educational system within a social network system applied to the enterprise context. The integration glue is provided by using and harmonizing several existing upper ontologies also furthering semantic interoperability.

This proposal adopts an approach based on a synergy between collaborative learning theories, semantic technologies and soft computing techniques to improve the performances of a research community. It is proposed a model of Virtual Organization based on three interconnected layers that can facilitate in-time retrieval of geo-located competences and skills, the recall of ad hoc services for setting a collaborative-oriented workspace and the access to geo-referenced resource repositories. This model proposes an experimental use of the Semantic Web (Domain Ontologies, Taxonomies, Upper Ontologies, etc...) and we will explain the added value it can bring to the Knowledge Communities research field.

The paper presents the main findings of the ELeGI project, namely its learning model and software architecture to support the creation and execution of complex learning processes.The learning model defined in ELeGI promotes and supports a learning paradigm centred on knowledge construction using experiential based and collaborative learning approaches in a contextualised, personalised and ubiquitous way.The software architecture has been designed and developed taking into account the learning model for the personalisation of complex learning experiences.In order to validate our results, the paper presents and describes a case study relating to the implementation of a Unit of Learning for explanation of the Torricelli's law, and its execution on top of the Service Oriented Architecture.

Nowadays, the semantic Web technologies are exploited also in the e-learning domain in order to provide personalized and adaptive learning experiences, semantic annotation of learning contents and learner profiling. The approaches of the Web 2.0, instead, are used to implement and deploy knowledge exchange services based on the concept of social collaboration. In this work, we propose an approach resulted from the convergence between semantic Web and social Web to manage, agilely and easily, the contingent learning needs of workers within organizations. Our intention is to support the use of natural languages to express the learning needs for either driving the automatic generation of learning units or effectively adapting learning pathways.

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.

The purpose of this paper is to propose an overview of the knowledge virtual enterprise model, where the virtual enterprise vision is extended with knowledge-based assets in order to provide an agreement model to support the interoperability among organizations. Every enterprise or organization, by itself, is a source of original knowledge that, if exploited, can contribute to its competitiveness. If this is true inside the enterprise walls, it is more relevant when extended to virtual enterprises, especially when they operate in a tumultuous and unsettled context, like ICT, strongly bound to the so called soft skills and even more to the capability of carrying out just-in-time knowledge take-over and transfer. In order to explain the advantages of the knowledge virtual enterprise model we define some real-world business scenarios, to be executed within the context of a knowledge virtual enterprise instance. The scenarios are based on the idea that several organizations could put together their competences, human resources, expertise, technologies, etc. to carry out complex project activities, requiring resources that are usually difficult to be found in a single organization. The scenarios are particularly focused on how the knowledge virtual enterprise model can support personalized, contextualized, effective and efficient e-learning at work experiences. Finally, the knowledge virtual enterprise model vision is concretized through the description of a feasible technological mapping between its main concerns and existing software technologies and specifications.

In this paper, starting from the limitations and constrains of traditional human learning approaches, we outline new suitable approaches to education and training in future knowledge based society. In our vision, learning and teaching are no longer standalone activities but complex, conversational and experiential-based processes implying collaboration, direct experience, mutual trust and shared interests. We identify characteristics of the environments suitable for these processes, and we compare different enabling technology infrastructures in order to justify why the semantic grid for human learning, that is a particular enhanced instance of the traditional semantic grid, is the most appropriate infrastructure to build our vision on. Finally, we present a realistic learning scenario as a case study, proving the effectiveness of our innovative learning approaches for future education and training.

Sensor data fusion imposes a number of novel requirements on query languages and query processing techniques. A spatial/temporal query language called /spl Sigma/QL has been proposed to support the retrieval and fusion of multimedia information from multiple sources and databases. In this paper we investigate fusion techniques, multimedia data transformations and /spl Sigma/QL query processing techniques for sensor data fusion. Fusion techniques including fusion by the merge operation, the detection of moving objects, and the incorporation of belief values, have been developed. An experimental prototype has been implemented and tested to demonstrate the feasibility of these techniques.