Transition of legacy systems to semantically enabled applications:TAO method and tools

Despite expectations being high, the industrial take-up of Semantic Web technologies in developing services and applications has been slower than expected. One of the main reasons is that many legacy systems have been developed without considering the potential of theWeb in integrating services and sharing resources.Without a systematic methodology and proper tool support, the migration from legacy systems to SemanticWeb Service-based systems can be a tedious and expensive process, which carries a significant risk of failure. There is an urgent need to provide strategies, allowing the migration of legacy systems to Semantic Web Services platforms, and also tools to support such strategies. In this paper we propose a methodology and its tool support for transitioning these applications to Semantic Web Services, which allow users to migrate their applications to Semantic Web Services platforms automatically or semi-automatically. The transition of the GATE system is used as a case study

Semi-automatic generation of quizzes and learning artifacts from Linked Data

In this position paper, we illustrate how Linked Data can be effectively used in a Technology-enhanced Learning scenario. Specifically, we aim at using structured data to semi-automatically generate artifacts to support learning delivery and assessment: natural language facts, Q&A systems and quizzes, also used with a gaming favour, can be creatively generated to help teachers and learners to support and improve the learning path. Moreover, those artifacts can in turn be published on the Web as Linked Data, thus directly contributing to make the Web a global data space also for learning purposes

Natural Language Interfaces to Conceptual Models

Accessing structured data in the form of ontologies currently requires the use of formal query languages (e.g., SeRQL or SPARQL) which pose significant difficulties for non-expert users. One way to lower the learning overhead and make ontology queries more straightforward is through a Natural Lan- guage Interface (NLI). While there are existing NLIs to structured data with reasonable performance, they tend to require expensive customisation to each new domain. Additionally, they often require specific adherence to a pre-defined syntax which, in turn, means that users still have to undergo training. In this thesis, we study the usability of NLIs from two perspectives: that of the developer who is customising the NLI system, and that of the end-user who uses it for querying. We investigate whether usability methods such as feedback and clarification dialogs can increase the usability for end users and reduce the customisation effort for the developers. To that end, we have developed two systems, QuestIO and FREyA, whose design, evaluation and comparison with similar systems form the core of the contribution of this thesis