Conventional knowledge graphs (KGs) are composed solely of entities, attributes, and
relationships, which poses challenges for enhancing multimodal knowledge representation …

Knowledge graph representation learning (KGRL) or knowledge graph embedding (KGE)
plays a crucial role in AI applications for knowledge construction and information …

Recent years have witnessed the release of many open-source and enterprise-driven
knowledge graphs with a dramatic increase of applications of knowledge representation and …

Link prediction in open knowledge graphs (OpenKGs) is crucial for applications like
question answering and recommendation systems. Existing OpenKG models leverage the …

Distributed optimization is a powerful paradigm to solve various problems in machine
learning over networked systems. Existing first-order optimization methods perform cheap …

In the near future, mobile networks are expected to broaden their services and coverage to
accommodate a larger user base and diverse user needs. Thus, they will increasingly rely …

Abstract Knowledge graphs (KGs) inherently lack reasoning ability which limits their
effectiveness for tasks such as question–answering and query expansion. KG embedding …

Abstract The incompleteness of Knowledge Base (KB) greatly limits the performance of
Question Answering (QA) system. Combining documents and incomplete KBs to develop QA …

Distributed optimization is a powerful paradigm to solve various problems in machine
learning over networked systems. Existing first-order optimization methods perform cheap …