A hyperknowledge approach to support dataset engineering

Abstract

The use of machine learning has become a common approach for solving complex problems across multiple application domains. As its usage often requires training and validation of models with large and het-erogeneous datasets, the engineering of these datasets becomes a critical task, although in many cases it does not follow any well-de_ned process. In this demonstration paper, we present a novel approach to dataset en-gineering, which comprises the construction, structuring, understanding, and reuse of datasets from a semantic perspective. Our approach uses a hybrid conceptual model called Hyperknowledge, which can semantically describe both symbolic and non-symbolic nodes, including representing the datasets' structure and enabling dataset retrieval/creation queries.