Abstract Machine learning (ML) and its parent technology trend, artificial intelligence (AI),
are deriving novel insights from ever larger and more complex datasets. Efficient and …

In a world rich in interconnected and complex data, the non-relational database paradigm
can better handle large volumes of data at high speed with a scale-out architecture, which …

Users combine attributes and types to describe and classify entities into categories. These
categories are fundamental for organising knowledge in a decentralised way acting as tags …

The self-rationalising capabilities of LLMs are appealing because the generated
explanations can give insights into the plausibility of the predictions. However, how faithful …

Measuring text complexity is an essential task in several fields and applications (such as
NLP, semantic web, smart education, etc.). The semantic layer of text is more tacit than its …

The processing of structured and semi-structured content on the Web has been gaining
attention with the rapid progress in the Linking Open Data project and the development of …

Question Answering (QA) is a field in computer science, which is concerned about building a
system, which can automatically answer a given question posed by user in natural …

The Big Data vision is based on the idea of supporting users and information systems with
large-scale and comprehensive data. Data sources based on new platforms such as open …

In the present paper, we conduct a comparative evaluation of a multitude of information-
seeking domains, using two well-known but fundamentally different algorithms for policy …

The Big Data vision is based on the idea of supporting users and information systems with
large-scale and comprehensive data. Data sources based on new platforms such as open …