Abstract Graph Neural Networks (GNNs) have emerged as a powerful tool for analyzing and
modeling graph-structured data. In recent years, GNNs have gained significant attention in …

The graph model is nowadays largely adopted to model a wide range of knowledge and
data, spanning from social networks to knowledge graphs (KGs), representing a successful …

Knowledge Graph Construction (KGC) can be seen as an iterative process starting from a
high quality nucleus that is refined by knowledge extraction approaches in a virtuous loop …

Abstract Knowledge graph embedding (KGE) is an efficient and scalable method for
knowledge graph completion. However, most existing KGE methods suffer from the …

Negative sampling (NS) is widely used in knowledge graph embedding (KGE), which aims
to generate negative triples to make a positive-negative contrast during training. However …

Knowledge graph embedding (KGE) is an efficient and scalable method for knowledge
graph completion tasks. Existing KGE models typically map entities and relations into a …

Pig disease knowledge graphs (KGs) are crucial for the prevention and treatment of pig
diseases. Due to the difficulty of knowledge mining in the field of traditional animal …

In this paper, we explore a new way for user targeting, where non-expert marketers could
select their target users solely given demands in natural language form. The key to this issue …

Few-shot temporal knowledge graph completion (TKGC) aims to predict missing facts in
temporal knowledge graphs (TKGs) when each relation has a limited number of available …

Abstract Knowledge graph completion (KGC) aims to fill in missing entities and relations
within knowledge graphs (KGs) to address their incompleteness. Most existing KGC models …