With the explosive growth of artificial intelligence (AI) and big data, it has become vitally
important to organize and represent the enormous volume of knowledge appropriately. As …

Knowledge graph completion aims to address the problem of extending a KG with missing
triples. In this paper, we provide an approach GenKGC, which converts knowledge graph …

Abstract Feature selection in Knowledge Graphs (KGs) is increasingly utilized in diverse
domains, including biomedical research, Natural Language Processing (NLP), and …

Answering complex first-order logic (FOL) queries on knowledge graphs is a fundamental
task for multi-hop reasoning. Traditional symbolic methods traverse a complete knowledge …

Graph convolutional networks (GCNs)—which are effective in modeling graph structures—
have been increasingly popular in knowledge graph completion (KGC). GCN-based KGC …

Abstract Knowledge graph (KG) reasoning utilizes two primary techniques, ie, rule-based
and KG-embedding based. The former provides precise inferences, but inferring via …

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 …

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

Knowledge graphs (KGs) capture knowledge in the form of head--relation--tail triples and
are a crucial component in many AI systems. There are two important reasoning tasks on …