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 …

Abstract Generalized Zero-Shot Learning (GZSL) identifies unseen categories by knowledge
transferred from the seen domain, relying on the intrinsic interactions between visual and …

Large language model (LLM) based knowledge graph completion (KGC) aims to predict the
missing triples in the KGs with LLMs and enrich the KGs to become better web infrastructure …

Abstract Compositional Zero-Shot Learning (CZSL) aims to recognize novel concepts
formed by known states and objects during training. Existing methods either learn the …

As a crucial extension of entity alignment (EA), multi-modal entity alignment (MMEA) aims to
identify identical entities across disparate knowledge graphs (KGs) by exploiting associated …

Generative Zero-shot learning (ZSL) learns a generator to synthesize visual samples for
unseen classes which is an effective way to advance ZSL. However existing generative …

Zero-shot learning (ZSL) recognizes the unseen classes by conducting visual-semantic
interactions to transfer semantic knowledge from seen classes to unseen ones supported by …

The tasks of few-shot, one-shot, and zero-shot learning—or collectively “low-shot
learning”(LSL)—at first glance are quite similar to the long-standing task of class imbalanced …

Abstract Zero-Shot Learning (ZSL) involves transferring knowledge from seen classes to
unseen classes by establishing connections between visual and semantic spaces …