The Semantic Web represents a huge information space where an increasing number of datasets, described in RDF, are made available to users and applications. In this context, the data is not constrained by a predefined schema. In RDF datasets, the schema may be incomplete or even missing. While this offers high flexibility in creating data sources, it also makes their use difficult. Several works have addressed the problem of automatic schema discovery for RDF datasets, but existing approaches rely only on the explicit information provided by the data source, which may limit the quality of the results. Indeed, in an RDF data source, an entity is described by explicitly declared properties, but also by implicit properties that can be derived using reasoning rules. These implicit properties are not considered by existing schema discovery approaches. In this work, we propose a first contribution towards a hybrid schema discovery approach capable of exploiting all the semantics of a data source, which is represented not only by the explicitly declared triples, but also by the ones that can be inferred through reasoning. By considering both explicit and implicit properties, the quality of the generated schema is improved. We provide a scalable design of our approach to enable the processing of large RDF data sources while improving the quality of the results. We present some experiments which demonstrate the efficiency of our proposal and the quality of the discovered schema.

Learning disabilities present significant barriers in the lives of individuals, particularly children and students, as they can impede their learning process and skill development. Dysgraphia, a form of learning disability, can adversely affect an individual’s writing ability. While various approaches have been proposed to detect learning disorders, there is a lack of methods for tracking the progression of these disorders. In this work, we propose an intelligent model for tracking the evolution of dysgraphia. Our approach utilizes a probabilistic machine learning algorithm to compute a Dysgraphic class score for each individual. By computing this score at different intervals, we can monitor the individual’s progress over time. To achieve this, we trained various probabilistic classifiers and fuzzy clustering algorithms on a labeled dataset to select the model with the best performance for tracking. Our experimental evaluation demonstrates that our model successfully tracks the evolution of individuals with dysgraphia.

In recent years, there has been a significant increase in the volume of data and the number of datasets in the healthcare industry, and this trend is expected to continue and intensify. Various strategies are being developed to analyse the data. Nevertheless, these strategies are extensively segregated according to the specific data formats and disorders. Privacy-preserving Hybrid Learning Framework for Healthcare. The framework introduces an hybrid learning technique in order to achieve efficient decision-making. To tackle the challenge of interoperability and heterogeneity with multiple data sources, we propose to integrate a data meshing approach. Furthermore, this paper identifies the potential privacy challenges for machine learning-based healthcare applications that operate with multiple data sources and demand the excessive computation of cloud computing. In addition, we present a comprehensive use case for forecasting cardiovascular disease. The detailed use case and scenarios highlight how our proposal can improve the decision-making process.

Drug traceability is currently a very challenging area given the complexity of several issues, including drug quality and counterfeit medications. The counterfeited drugs have a major impact on human life, treatment outcomes and economic burden. To deal with these issues, we propose a semantic blockchain-based system for drug traceability that aims at detecting counterfeit drugs in order to improve the patients’ safety and quality of life as well as eliminating manufacturers’ potential loss and increasing their revenue. Our proposal is based on blockchain and semantic web technologies to enhance the representation capability of data in the pharmaceutical supply chain.