Large language models (LLMs) have shown remarkable generalization capability with
exceptional performance in various language modeling tasks. However, they still exhibit …

A fundamental task for knowledge graphs (KGs) is knowledge graph completion (KGC). It
aims to predict unseen edges by learning representations for all the entities and relations in …

Multi-modal knowledge graph completion (MMKGC) aims to automatically discover the
unobserved factual knowledge from a given multi-modal knowledge graph by collaboratively …

Graph neural networks (GNNs), which effectively use topological structures in the
knowledge graphs (KG) to embed entities and relations in low-dimensional spaces, have …

Abstract Knowledge graph embedding (KGE), which maps entities and relations in a
knowledge graph into continuous vector spaces, has achieved great success in predicting …

Recently, a large amount of work has emerged for knowledge graph completion (KGC),
which aims to reason over known facts and to infer the missing links. Meanwhile, contrastive …

Despite their capacity to convey knowledge, most existing knowledge graphs (KGs) are
created for specific domains using low-resource data sources, especially those in non …

Graph Neural Networks (GNNs) have demonstrated great success in Knowledge Graph
Completion (KGC) by modeling how entities and relations interact in recent years. However …

Document-level relation extraction (RE) aims to identify relations between entities across
multiple sentences. Most previous methods focused on document-level RE under full …

This paper studies speculative reasoning task on real-world knowledge graphs (KG) that
contain both\textit {false negative issue}(ie, potential true facts being excluded) and\textit …