Artificial intelligence (AI) is currently regaining enormous interest due to the success of
machine learning (ML), and in particular deep learning (DL). Image analysis, and thus …

We propose the LLMs4OL approach, which utilizes Large Language Models (LLMs) for
Ontology Learning (OL). LLMs have shown significant advancements in natural language …

The unification of statistical (data-driven) and symbolic (knowledge-driven) methods is
widely recognized as one of the key challenges of modern AI. Recent years have seen a …

It is beneficial to automate the process of deriving concept hierarchies from corpora since a
manual construction of concept hierarchies is typically a time-consuming and resource …

Background Ontologies are fundamental components of informatics infrastructure in
domains such as biomedical, environmental, and food sciences, representing consensus …

The deterministic AI system under review is an alternative to neural-network-based machine
learning. In its application fields, which are science, technology, engineering, and business …

Exponential growth of biomedical literature and clinical data demands more robust yet
precise computational methodologies to extract useful insights from biomedical literature …

We propose a set of compositional design patterns to describe a large variety of systems that
combine statistical techniques from machine learning with symbolic techniques from …

Suicidal ideation (SI), as a psychiatric emergency, requires immediate assistance and
intervention. Most people with SI do not actively seek help from mental health professionals …

Accident databases are used to learn from past accidents and avoid future accidents in the
chemical process industry. Classical accident databases can be tedious to use because the …