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 heterogeneous datasets, the engineering of these datasets becomes a critical task, although in many cases it does not follow any well-defined process. In this demo, we present a novel approach to dataset engineering, 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 queries for dataset retrieval/creation.