Deep learning has been the mainstream technique in natural language processing (NLP)
area. However, the techniques require many labeled data and are less generalizable across …

Knowledge graph (KG) refinement refers to the process of filling in missing information,
removing redundancies, and resolving inconsistencies in KGs. With the growing popularity …

Recommender systems have been widely deployed in many real-world applications, but
usually suffer from the long-standing user cold-start problem. As a promising way, Cross …

Event extraction (EE) is a crucial research task for promptly apprehending event information
from massive textual data. With the rapid development of deep learning, EE based on deep …

Few-shot Learning (FSL) is aimed to make predictions based on a limited number of
samples. Structured data such as knowledge graphs and ontology libraries has been …

In this paper, we investigate a realistic but underexplored problem, called few-shot temporal
knowledge graph reasoning, that aims to predict future facts for newly emerging entities …

Aiming at expanding few-shot relations' coverage in knowledge graphs (KGs), few-shot
knowledge graph completion (FKGC) has recently gained more research interests. Some …

Knowledge graphs (KGs) consisting of a large number of triples have become widespread
recently, and many knowledge graph embedding (KGE) methods are proposed to embed …

Knowledge graphs (KGs), as a structured form of knowledge representation, have been
widely applied in the real world. Recently, few-shot knowledge graph completion (FKGC) …

Knowledge graph completion (KGC) has become a focus of attention across deep learning
community owing to its excellent contribution to numerous downstream tasks. Although …