In the last few years, we have witnessed the emergence of several knowledge graphs that explicitly describe research knowledge with the aim of enabling intelligent systems for supporting and accelerating the scientific process. These resources typically characterize a set of entities in this space (e.g., tasks, methods, evaluation techniques, proteins, chemicals), their relations, and the relevant actors (e.g., researchers, organizations) and documents (e.g., articles, books). However, they are usually very partial representations of the actual research knowledge and may miss several relevant facts. In this paper, we introduce SciCheck, a new triple classification approach for completing scientific statements in knowledge graphs. SciCheck was evaluated against nine alternative approaches on seven benchmarks, yielding excellent results. Finally, we provide a real-world use case and applied SciCheck to the Artificial Intelligence Knowledge Graph (AI-KG), a large-scale automatically-generated open knowledge graph including 1.2M statements extracted from the 333K most cited articles in the field of Artificial Intelligence, and generated a new version of this knowledge graph with 300K additional triples.

Many solutions for coarse geolocating of users at the time they post a message exist. However, for many important applications, like traffic monitoring and event detection, finer geolocation at the level of city neighborhoods, i.e., at a sub-city level, is needed. Data-driven approaches often do not guarantee good accuracy and efficiency due to the higher number of sub-city level positions to be estimated and the low availability of balanced and large training sets. We claim that external information sources overcome limitations of data-driven approaches in achieving good accuracy for sub-city level geolocation and we present a knowledge-driven approach achieving good results once the reference area of a message is known. Our algorithm, called Sherloc, exploits toponyms in the message, extracts their semantic from a geographic gazetteer, and embeds them into a metric space that captures the semantic distance among them. We identify the semantically closest toponyms to a message and then cluster them with respect to their spatial locations. Sherloc requires no prior training, it can infer the location at sub-city level with high accuracy, and it is not limited to geolocating on a fixed spatial grid.

In recent years, there has been a lot of discussion around ethics in IT and AI. Many researchers and organisations have proposed guidelines to address privacy, fairness, and explainability challenges for creating trustworthy AI. In this paper, we discuss ethical principles in the context of AI and their significance in developing trustworthy AI solutions. We consider the problem of the categorisation of ethical principles in IT. We concentrate our discussion on privacy, fairness, and explainability. These principles, we believe, meaningfully contribute to the trust of AI systems. We overview the available privacy regulations in the EU and US. We also look at how to achieve compliance with them, including private data detection, data anonymisation techniques and toolkits. From a practical perspective, we analyse fairness and bias problems. We discuss the issue of fairness assessment and metrics. To improve the fairness of AI solutions, an enormous number of techniques have been developed. We also focus on fairness improvement techniques and a few popular toolkits in which these techniques are implemented. Explainability is another ethical principle discussed. It is one of many socially important properties, as it ensures understanding of AI system decision-making and transparency in inspection. Ensuring explainability is important for high-risk applications in healthcare, finance and criminal justice. Finally, we outline approaches that help in the level of explainability. With this review and analysis, we contribute to the knowledge of available techniques and toolkits that can be used by AI practitioners as an initial step in implementing ethical principles into AI solutions.