Ontology-based question generation is an important application of semantic-aware systems that enables the creation of large question banks for diverse learning environments. The effectiveness of these systems, both in terms of the calibre and cognitive difficulty of the resulting questions, depends heavily on the quality and modelling approach of the underlying ontologies, making it crucial to assess their fitness for this task. To date, there has been no comprehensive investigation into the specific ontology aspects or characteristics that affect the question generation process. Therefore, this paper proposes a set of requirements and task-specific metrics for evaluating the fitness of ontologies for question generation tasks in pedagogical settings. Using the ROMEO methodology (a structured framework used for identifying task-specific metrics), a set of evaluation metrics have been derived from an expert assessment of questions generated by a question generation model. To validate the proposed metrics, we apply them to a set of ontologies previously used in question generation to illustrate how the metric scores align with and complement findings reported in earlier studies. The analysis confirms that ontology characteristics significantly impact the effectiveness of question generation, with different ontologies exhibiting varying performance levels. This highlights the importance of assessing ontology quality with respect to Automatic Question Generation (AQG) tasks.

This paper presents a novel approach to ontology-based automatic question generation (AQG), which aims to facilitate the creation of ‘complex’ educational questions, i.e. questions requiring larger knowledge cover and higher cognitive/reasoning processes. We leverage on the notion of Query Graphs, a graph-like structure that can represent natural language queries through appropriate mappings, previously used for semantic parsing. We propose the use of Query Graphs as a formalism for representing templates that incorporate multiple ontology-based constraints during question generation, which can then guide the selection of relevant ontology elements and properties. By incorporating these constraints, our approach elevates the level of reasoning required to answer the questions, therefore overcoming the limitations of existing methodologies. We evaluate the approach by modelling templates for AQG and comparing the resulting questions with existing state-of-the-art methods, on performance based on complexity levels. The empirical evaluation demonstrates that our model is indeed able to generate questions that demand higher cognitive skills.

“Social emotion” in text mining refers to the emotion experienced by the reader exposed to a text, as opposed to the emotion conveyed from the author’s perspective. Mining the cause of social emotion from text is a new challenging task with a wide range of applications, but its progress is hindered by the lack of annotated datasets. In this paper, we release the first English dataset for social emotion cause. The dataset is based on a well-established corpus of Facebook posts, and it was annotated through a crowdsourcing experiment. Together with the dataset, we provide two baseline models to be used as benchmarks for future studies.

Social emotion prediction is concerned with the prediction of the reader’s emotion when exposed to a text. In this paper, we propose a transfer learning approach to social emotion prediction, where the source task is writer’s emotion prediction, an area in which models are advanced due to the rich literature and availability of large and high-quality training datasets. We utilized a pre-trained writer’s emotion prediction model to predict the writer’s emotion in comments, then we aggregated the emotions and trained a classifier to predict social emotion for posts. Results show that pre-trained models for writer’s emotion prediction can improve the prediction of social emotion. Furthermore, we demonstrate that our proposed model outperforms popular models in terms of F1-score and performs similarly to the best model in terms of Acc@1.

Ontology reuse is a complex process that requires the support of methodologies and tools to minimise errors and to keep the ontologies consistent and coherent. Although the vast majority of ontology engineering methodologies include a reuse phase, and reuse has been investigated for different tasks and purposes (e.g.ontology integration), this body of work does not seem to translate into practice, neither in the form of strict criteria for reuse, nor as a set of community proposed guidelines. In this paper, we report the salient results from a study aimed at ontology developers and practitioners, whose objective is to gain an insight into the gap between the theory and the practice of ontology reuse. Thefocus of our study is to gain practitioners' views on i) their preferred reuse approaches; ii) the types of ontologies they tend to reuse (e.g. specific domain ontologies or upper-level ontologies)iii) what reporting information they deem useful when deciding which ontology to reuse; iv) what are the main reasons deterring them from reusing an ontology. Our findings confirm and extend established results from the literature, but in addition, the study provides a fresh view on the practice of reuse with an explicit focus on highly experienced developers and moderately experienced ones. The study corroborates the need for a comprehensive set of recommendations, that are widely accepted by the community, and are possibly implemented in development tools.

Higher Education is witnessing a mental health crisis without precedents. Learning analytics offer powerful tools to monitor students engagement, but would instructors in Virtual Learning Environments (VLEs) benefit from being able to monitor mental well-being as well? And how can this be implemented in a way that is not disruptive to education? We have devised a conceptual framework addressing this issue, which incorporate the role of the online tutor into an environment "observing" student communicative acts. The conceptual framework is evaluated in a series of experiments and case studies, aimed at assessing the feasibility and appreciate the usefulness of single potential components, and on the basis of expert commentaries.

This paper describes a computer assisted interview for children and vulnerable adults. The system implements a “triadic interview” interaction since it is used as a focus point between the child and the clinician, whose dialogue is mediated by the tool. The tool helps children express their feelings and experiences, by making use of an “emotion palette” and a set of sub-tools developed on paper by children and transformed into computer based depictions. The tool has been extensively evaluated in clinical practice, providing a strong indication of its ability to improve the quality of the interaction with children.

E-Health is defined by the World Health Organisation as 'the combined use of electronic communication and information technology in the health sector' (WHO http:// www.who.int/topics/ehealth

Many websites nowadays allow social networking between their users in an explicit or implicit way. In this work, we show how the theory of argumentation schemes can provide a valuable help to formalize and structure on-line discussions and user opinions in decision support and business oriented websites that hold social networks among their users. A real study case is considered and analysed. Then, guidelines for website and system design are provided to enhance social decision support and recommendations with argumentation.

This paper presents an ontology based approach to the production of personalised motivational messages to the user of a e-Health service. The service is aimed at encouraging diabetic patients to do physical activity. The textual messages have an overall predetermined structure, and need to be personalised to the user's characteristics, and to the context in which the message needs to be generated.

This article reviews recent advances in the interdisciplinary area lying between artificial intelligence and the theory of argumentation. The article has two distinct foci: first, examining the ways in which argumentation has inspired new models of logical and computational intelligence, and second, exploring how AI techniques have been used and extended to model and handle real world argument in a wide variety of domains including law, education, medicine, and e-commerce. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1–15, 2007.