Generative Knowledge Graph Construction (KGC) refers to those methods that leverage the
sequence-to-sequence framework for building knowledge graphs, which is flexible and can …

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 …

Recently, prompt-tuning has achieved promising results for specific few-shot classification
tasks. The core idea of prompt-tuning is to insert text pieces (ie, templates) into the input and …

Triple extraction is an essential task in information extraction for natural language
processing and knowledge graph construction. In this paper, we revisit the end-to-end triple …

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 …

Pre-trained language models have contributed significantly to relation extraction by
demonstrating remarkable few-shot learning abilities. However, prompt tuning methods for …

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 …

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 …

Information extraction tasks such as entity relation extraction and event extraction are of
great importance for natural language processing and knowledge graph construction. In this …