Big data is a rapidly growing field, and new developments are constantly emerging to
address various challenges. One such development is the use of federated learning for …

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

Federated learning (FL) is a machine learning paradigm, which enables multiple and
decentralized clients to collaboratively train a model under the orchestration of a central …

Abstract Knowledge Graph (KG) has attracted more and more companies' attention for its
ability to connect different types of data in meaningful ways and support rich data services …

Knowledge graphs (KGs), which consist of triples, are inherently incomplete and always
require completion procedure to predict missing triples. In real-world scenarios, KGs are …

In federated learning (FL), multiple parties collaborate to train a global model by aggregating
their local models while kee** private training sets isolated. One problem hindering …

With the development of social media, the internet, and sensing technologies, multimodal
data are becoming increasingly common. Integrating these data into knowledge graphs can …

Recently, dual-target cross-domain recommendation (DTCDR) has been proposed to
alleviate the data sparsity problem by sharing the common knowledge across domains …

Maintenance is a vital aspect of ensuring the reliability, availability, and safety of machinery
and systems. Traditional maintenance approaches, such as corrective and preventive …

Federated knowledge graph reasoning (FedKGR) aims to perform reasoning over different
clients while protecting data privacy, drawing increasing attention to its high practical value …