A common solution to the semantic heterogeneity problem is to perform knowledge graph
(KG) extension exploiting the information encoded in one or more candidate KGs, where the …

We propose a novel approach to the problem of semantic heterogeneity where data are
organized into a set of stratified and independent representation layers, namely: conceptual …

Smart devices generate vast amounts of big data, mainly in the form of sensor data. While
allowing for the prediction of many aspects of human behaviour (eg, physical activities …

In recent years, the design and production of information systems have seen significant
growth. However, these information artefacts often exhibit characteristics that compromise …

The development of domain ontological models, though being a mature research arena
backed by well-established methodologies, still suffer from two key shortcomings. Firstly, the …

Abstract Knowledge Representation (KR) and facet-analytical Knowledge Organization (KO)
have been the two most prominent methodologies of data and knowledge modelling in the …

One of the significant barriers to the training of statistical models on knowledge graphs is the
difficulty that scientists have in finding the best input data to address their prediction goal. In …

Recent work in Machine Learning and Computer Vision has provided evidence of
systematic design flaws in the development of major object recognition benchmark datasets …

The aim of transfer learning is to reuse learnt knowledge across different contexts. In the
particular case of cross-domain transfer (also known as domain adaptation), reuse happens …

We are living in an era of global digital platforms, eco-systems of algorithmic processes that
serve users worldwide. However, the increasing exposure to diversity online–of information …