Knowledge Graphs (KGs) play a pivotal role in advancing various AI applications, with the
semantic web community's exploration into multi-modal dimensions unlocking new avenues …

Machine learning (ML), especially deep neural networks, has achieved great success, but
many of them often rely on a number of labeled samples for supervision. As sufficient …

Semantic embedding of knowledge graphs has been widely studied and used for prediction
and statistical analysis tasks across various domains such as Natural Language Processing …

Zero-shot Learning (ZSL), which aims to predict for those classes that have never appeared
in the training data, has arisen hot research interests. The key of implementing ZSL is to …

Zero-shot learning (ZSL) which aims at predicting classes that have never appeared during
the training using external knowledge (aka side information) has been widely investigated …

Knowledge Graph (KG) and its variant of ontology have been widely used for knowledge
representation, and have shown to be quite effective in augmenting Zero-shot Learning …

Detection of fake news has spurred widespread interests in areas such as healthcare and
Internet societies, in order to prevent propagating misleading information for commercial and …

Zero-shot learning (ZSL) which aims to deal with new classes that have never appeared in
the training data (ie, unseen classes) has attracted massive research interests recently …

Zero-Shot Relational Learning (ZSRL), strives to predict relations that have not been
observed during training, presenting a considerable challenge in terms of model …

The large volume of open data on the Web is expected to be reused and create value.
Finding the right data to reuse is a non-trivial task addressed by the recent dataset search …