Antonella Carbonaro

Towards an Automatic Forum Summarization to Support Tutoring

The process of summarizing information is becoming increasingly important in the light of recent advances in resource creation and distribution and the resulting influx of large numbers of information in everyday life. These advances are also challenging educational institutions to adopt the opportunities of distributed knowledge sharing and communication. Among the most recent trends, the availability of social communication networks, knowledge representation and of activate learning gives rise for a new landscape of learning as a networked, situated, contextual and life-long activities. In this scenario, new perspectives on learning and teaching processes must be developed and supported, relating learning models, content-based tools, social organization and knowledge sharing.

WordNet-based Summarization to Enhance Learning Interaction Tutoring

The process of summarizing information is becoming increasingly important in the light of recent advances in resource creation/distribution technology and the resulting infux of large amounts of information in everyday life. These advances are also challenging educational institutions to adopt the opportunities of distributed knowledge sharing and communication. The paper describes a summarization system to support tutors in managing student communication and interaction within a learning framework. Results show the adequacy of the system in identifying a good content summarization and then in improving the effciency and effectiveness of the context in which summarization can be integrated.

Improving Web Search and Navigation Using Summarization Process

The paper presents a summarization process for enabling personalized searching framework facilitating the user access and navigation through desired contents. The system will express key concepts and relationships describing resources in a formal machine-processable representation. A WordNet-based knowledge representation could be used for content analysis and concept recognition, for reasoning processes and for enabling user-friendly and intelligent content exploration.

Towards Consistent Data Representation in the IoT Healthcare Landscape

Nowadays, the enormous volume of health and fitness data gathered from IoT wearable devices offers favourable opportunities to the research community. For instance, it can be exploited using sophisticated data analysis techniques, such as automatic reasoning, to find patterns and, extract information and new knowledge in order to enhance decision-making and deliver better healthcare. However, due to the high heterogeneity of data representation formats, the IoT healthcare landscape is characterised by an ubiquitous presence of data silos which prevents users and clinicians from obtaining a consistent representation of the whole knowledge. Semantic web technologies, such as ontologies and inference rules, have been shown as a promising way for the integration and exploitation of data from heterogeneous sources. In this paper, we present a semantic data model useful to: (1) consistently represent health and fitness data from heterogeneous IoT sources; (2) integrate and exchange them; and (3) enable automatic reasoning by inference engines.

Semantic Description of Healthcare Devices to Enable Data Integration

With the blooming of data created for example by IoT devices, the possibility to handle all information coming from healthcare applications is becoming increasingly challenging. Cognitive computing systems can be used to analyse large information volume by providing insights and recommendations to represent, access, integrate, and investigate data in order to improve outcomes across many domains, including healthcare. This paper presents an ontology-based system for the eHealth domain. It provides semantic interoperability among heterogeneous IoT devices and facilitates data integration and sharing. The novelty of the proposed approach lies in exploiting semantic web technologies to explicitly describe the meaning of sensor data and define a common communication strategy for information representation and exchange.

Integrating Heterogeneous Data of Healthcare Devices to enable Domain Data Management

The growth of data produced for example by IoT devices has playing a major role in developing healthcare applications able to effectiveness handle the vast amount of information. The challenge lies in representing volumes of data, integrating and understanding their various formats and sources. Cognitive computing systems offer promise for analysing, accessing, integrating, and investigating data in order to improve outcomes across many domains, including healthcare. This paper presents an ontology-based system for the eHealth domain. It provides semantic interoperability among heterogeneous IoT fitness and wellness devices and facilitates data integration and sharing. The novelty of the proposed approach lies in exploiting semantic web technologies to explicitly describe the meaning of sensor data and define a common communication strategy for information representation and exchange.

Good practices to influence engagement and learning outcomes on a traditional introductory programming course

ABSTRACT There have been many successful examples of new methodological approaches developed to help students in computer programming courses. Of these approaches, the peer assessment mechanism could be useful in providing students with opportunities to learn from one another, improve their learning experience and reach efficient learning outcomes. The paper presents and analyses an improved system based on the received evaluations of a previously developed web-based programming-assisted environment. This system automatically manages the peer code review process and delivers feedback to peers in a manner that favours the incremental learning of the concepts presented throughout the course. The experimental results are focussed on the impact of this system on students’ programming competence, time management capabilities and student engagement.

Automatic Concept Extraction in Semantic Summarization Process

The Semantic Web offers a generic infrastructure for interchange, integration and creative reuse of structured data, which can help to cross some of the boundaries that Web 2.0 is facing. Currently, Web 2.0 offers poor query possibilities apart from searching by keywords or tags. There has been a great deal of interest in the development of semantic-based systems to facilitate knowledge representation and extraction and content integration [1], [2]. Semantic-based approach to retrieving relevant material can be useful to address issues like trying to determine the type or the quality of the information suggested from a personalized environment. In this context, standard keyword search has a very limited effectiveness. For example, it cannot filter for the type of information, the level of information or the quality of information.

Discovery and Learning in a Semantic Framework

The paper presents a web based system that aims to improve Web exploration by enabling users to discover knowledge associated to his need. Indeed, in exploratory search, the user is willing not only to find documents relevant with respect to his query but he is also interested in learning, discovering and understanding knowledge on complex and sometimes unknown topics. We rely on DBpedia to explore the semantics of keywords thus suggesting potentially interesting related topics or keywords to the user and enabling user-friendly and intelligent content discovery.