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 …

In knowledge graph completion (KGC), predicting triples involving emerging entities and/or
relations, which are unseen when the KG embeddings are learned, has become a critical …

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 …

We present Archer, a challenging bilingual text-to-SQL dataset specific to complex
reasoning, including arithmetic, commonsense and hypothetical reasoning. It contains 1,042 …

Event extraction is an important, but challenging task. Many existing techniques decompose
it into event and argument detection/classification subtasks, which are complex structured …

Panoptic segmentation methods assign a known class to each pixel given in input. Even for
state-of-the-art approaches, this inevitably enforces decisions that systematically lead to …

Unsupervised commonsense reasoning (UCR) is becoming increasingly popular as the
construction of commonsense reasoning datasets is expensive, and they are inevitably …

Zero-shot learning (ZSL) aims to predict unseen classes without using samples of these
classes in model training. The ZSL has been widely used in many knowledge-based models …

E-commerce platforms should provide detailed product descriptions (attribute values) for
effective product search and recommendation. However, attribute value information is …