In this work, an Automated Ontology Generator System (AOGS) is developed to autonomously create and populated ontologies based on the linked data paradigm. AOGS generates context-specific ontologies and populates these with information extracted from various linked data sources and pre-existing ontologies. AOGS allows reducing the time and effort required to create an ontology, ensuring consistency and accuracy in the ontology, and that domain experts to focus on higher-level tasks such as ontology refinement and evaluation. MEDAWEDE methodology is used to develop AOGS, which is then applied to create ontologies for COVID-19 and Energy. The quality of these ontologies is evaluated using precision, recall and f-measure metrics, which yield values between 0.9 and 0.7. The ontologies are also validated using Competence Questions, Consistency, and Quality Validation methods, all of which show good results.

In a smart environment like the smart grids, it is necessary to have knowledge models that allow solving emerging problems. However, large datasets are required to automatically create these models, which in the vast majority of cases are not available. Therefore, in these environments is essential to have a data generator for each context. In this paper, we propose a synthetic data generation system based on the variational autoencoder (VAE) technique and linked data paradigm, to create larger datasets from small datasets acting as samples. Specifically, a Linked Data-based dataset extractor is proposed, which allows obtaining samples of data in a particular context. Then, a VAE is trained with these samples of data available in a specific context, to learn the latent distribution that characterizes the dataset, allowing the production of new records that are similar to those of the original dataset. Finally, several case studies in the field of energy management are carried out. In one of them, the process that follows our approach is described in detail; and then, another 3 more are considered to evaluate its ability to automate the data generation process for smart energy management. The results show how our synthetic data generation system can be used to obtain synthetic datasets in different energy contexts.

In Machine Learning (ML), there is a lot of research that seek to automate specific processes carried out by data scientists in the generation of knowledge models (predictive, classification, clustering, etc.); however, an open problem is to find mechanisms that allow conferring the ability of self-learning. Thus, a meta-learning mechanism is required to allow ML techniques to self-adapt in order to improve their performance in problem solving, and even in some cases, to induce the learning algorithm itself. In this context, our research defines a meta-learning architecture using Linked Data (LD) for the automatic generation of knowledge models. Specifically, this intelligent architecture is formed by the layers of Knowledge Sources, Meta-Knowledge and Knowledge Modelling, to unify all processes to guarantee a Meta-Learning process. The Knowledge Sources layer is responsible for providing semantic knowledge about the processes of generation of knowledge models; the Meta-Knowledge layer is responsible for controlling the different processes and strategies for the automatic generation of knowledge models; and finally, the Knowledge Modelling layer is responsible for executing ML tasks defined by the Meta-Knowledge layer, among which are the tasks of feature engineering, ML algorithm configuration, model building, among others. Additionally, this article presents a case study to analyze the behavior of the different layers of the architecture, to generate knowledge models. Thus, the main contribution of this research is the definition of a Meta-Learning architecture for ML techniques, which takes advantage of the semantic information described as LD when generating the knowledge models. The preliminary results are very encouraging.