A review of data mining ontologies | Emerald Insight Books and journals Case studies Expert
Briefings Open Access Publish with us Advanced search A review of data mining ontologies …

Abstract Competency Questions (CQs) are natural language questions outlining and
constraining the scope of knowledge represented in an ontology. Despite that CQs are a …

Intelligent assistants often struggle with the complexity of spatiotemporal models used for
understanding objects and environments. The construction and usage of such models …

This data article reports on a new set of 234 competency questions for ontology
development and their formalisation into a set of 131 SPARQL-OWL queries. This is the …

We address the task of multi-target regression, where we generate global models that
simultaneously predict multiple continuous variables. We use ensembles of generalized …

Abstract Model-based systems engineering (MBSE) has become a new trend in systems
engineering. The core of MBSE is the use of models to formalize product development and …

Predictive maintenance often relies on the continuous monitorization of equipment behavior,
generally provided by sensors or by the very equipment. Additional data from management …

Abstract Data Analytics Solution (DAS) engineering often involves multiple tasks from data
exploration to result presentation which are applied in various contexts and on different …

This paper presents the AiD-EM Ontology, which provides a semantic representation of the
concepts required to enable the interoperability between multi-agent-based decision …

Multilabel classification (MLC) is a machine learning task where the goal is to learn to label
an example with multiple labels simultaneously. It receives increasing interest from the …