Large Language Models (LLMs) have taken Knowledge Representation--and the world--by
storm. This inflection point marks a shift from explicit knowledge representation to a renewed …

Knowledge graphs are widely used in industry and studied within the academic community.
However, the models applied in the development of knowledge graphs vary. Analysing and …

Large language models (LLMs), such as GPT-4, are revolutionizing software's ability to
understand, process, and synthesize language. The authors of this paper believe that this …

Recommender systems (RS), serving at the forefront of Human-centered AI, are widely
deployed in almost every corner of the web and facilitate the human decision-making …

Structured knowledge is important for many AI applications. Commonsense knowledge,
which is crucial for robust human-centric AI, is covered by a small number of structured …

Automated fact-checking, using machine learning to verify claims, has grown vital as
misinformation spreads beyond human fact-checking capacity. Large language models …

We introduce ReFinED, an efficient end-to-end entity linking model which uses fine-grained
entity types and entity descriptions to perform linking. The model performs mention …

In the current digitalization era, capturing and effectively representing knowledge is crucial
in most real-world scenarios. In this context, knowledge graphs represent a potent tool for …

Machine learning (ML), especially deep neural networks, has achieved great success, but
many of them often rely on a number of labeled samples for supervision. As sufficient …

With knowledge graphs (KGs) at the center of numerous applications such as recommender
systems and question answering, the need for generalized pipelines to construct and …