An Experiment in Retrofitting Competency Questions for Existing Ontologies

Competency Questions (CQs) are a form of ontology functional requirements expressed as natural language questions. Inspecting CQs together with the axioms in an ontology provides critical insights into the intended scope and applicability of the ontology. CQs also underpin a number of tasks in the development of ontologies e.g. ontology reuse, ontology testing, requirement specification, and the definition of patterns that implement such requirements. Although CQs are integral to the majority of ontology engineering methodologies, the practice of publishing CQs alongside the ontological artefacts is not widely observed by the community. In this context, we present an experiment in retrofitting CQs from existing ontologies. We propose RETROFIT-CQs, a method to extract candidate CQs directly from ontologies using Generative AI. In the paper we present the pipeline that facilitates the extraction of CQs by leveraging Large Language Models (LLMs) and we discuss its application to a number of existing ontologies

A Conceptual Framework for Real-Time Emotional-State Monitoring of Students in VLEs to Identify Students at Risk

Virtual Learning Environments (VLEs) feature rich textual data which lend itself naturally to the identification and monitoring of aspects of students’ interactions. While reducing attrition and improving performance remain the primary objectives of learning analytics, we contend that student contributed text can be used to learn about emotions and other extra-rational features. This would help provide a response to the recent cries for help from the sector, seeking a system looking to address the worrying mental health crisis trends. This paper addresses these issues by discussing the necessary mechanisms within a conceptual framework which would sit in a VLE and capture emotional state changes in the students’ interaction style or tone. For such a framework, the aim would be to help educators to carry out timely interventions when a potential cause of distress is identified. Experimental results on available datasets from education and psychology serve as a feasibility study for these tasks, and offer a perspective on the potential of the approach.</jats:p

An abstract argumentation approach for the prediction of analysts’ recommendations following earnings conference calls

Financial analysts constitute an important element of financial decision-making in stock exchanges throughout the world. By leveraging on argumentative reasoning, we develop a method to predict financial analysts' recommendations in earnings conference calls (ECCs), an important type of financial communication. We elaborate an analysis to select those reliable arguments in the Questions Answers (QA) part of ECCs that analysts evaluate to estimate their recommendation. The observation date of stock recommendation update may variate during the next quarter: it can be either the day after the ECC or it can take weeks. Our objective is to anticipate analysts' recommendations by predicting their judgment with the help of abstract argumentation. In this paper, we devise our approach to the analysis of ECCs, by designing a general processing framework which combines natural language processing along with abstract argumentation evaluation techniques to produce a final scoring function, representing the analysts' prediction about the company's trend. Then, we evaluate the performance of our approach by specifying a strategy to predict analysts recommendations starting from the evaluation of the argumentation graph properly instantiated from an ECC transcript. We also provide the experimental setting in which we perform the predictions of recommendations as a machine learning classification task. The method is shown to outperform approaches based only on sentiment analysis