Global datasphere is increasing fast, and it is expected to reach 175 Zettabytes by 20251.
However, most of the content is unstructured and is not understandable by machines …

The performance of applications, such as personal assistants and search engines, relies on
high-quality knowledge bases, aka Knowledge Graphs (KGs). To ensure their quality one …

Neurosymbolic artificial intelligence (AI) is an emerging branch of AI that combines the
strengths of symbolic AI and sub-symbolic AI. Symbolic AI is based on the idea that …

This study explores the use of Large Language Models (LLMs) for automatic evaluation of
knowledge graph (KG) completion models. Historically, validating information in KGs has …

As part of knowledge engineering workflows, semantic artifacts, such as ontologies,
knowledge graphs or semantic descriptions based on industrial standards, are often …

Knowledge graphs are invaluable assets for facilitating the dissemination of structured and
semantically enriched data to both humans and machines while ensuring its accuracy and …

Purpose The curation of ontologies and knowledge graphs (KGs) is an essential task for
industrial knowledge-based applications, as they rely on the contained knowledge to be …

Community enterprises aim to enhance products by standardizing goods and services
through the incorporation of knowledge. However, the absence of a centralized knowledge …

Anomaly detection is the process of discovering unusual or rare patterns in data that are
significantly different from the rest of the observations in a dataset. The importance of the …

The expansion of knowledge graphs (KGs) by new triples is an elementary process, which is
needed for enriching and extending the represented knowledge. In order to function …