Drug discovery is adapting to novel technologies such as data science, informatics, and
artificial intelligence (AI) to accelerate effective treatment development while reducing costs …

In this article, we focus on the opacity issue of sub-symbolic machine learning predictors by
promoting two complementary activities—symbolic knowledge extraction (SKE) and …

Foundation models in language and vision have the ability to run inference on any textual
and visual inputs thanks to the transferable representations such as a vocabulary of tokens …

Field compliance checking aims to check the compliance of site operations with applicable
construction safety regulations for detecting violations. Relation extraction provides an …

Answering complex logical queries on incomplete knowledge graphs is a challenging task,
and has been widely studied. Embedding-based methods require training on complex …

Querying knowledge graphs (KGs) using deep learning approaches can naturally leverage
the reasoning and generalization ability to learn to infer better answers. Traditional neural …

Abstract Knowledge graphs, modeling multi-relational data, improve numerous applications
such as question answering or graph logical reasoning. Many graph neural networks for …

Formulating and answering logical queries is a standard communication interface for
knowledge graphs (KGs). Alleviating the notorious incompleteness of real-world KGs, neural …

Abstract Temporal Knowledge Graph (TKG) is an extension of traditional Knowledge Graph
(KG) that incorporates the dimension of time. Reasoning on TKGs is a crucial task that aims …

Abstract Graph Neural Networks (GNN) is one of the promising machine learning areas in
solving real world problems such as social networks, recommender systems, computer …