Cancer is a leading cause of morbidity and mortality worldwide. While progress has been
made in the diagnosis, prognosis, and treatment of cancer patients, individualized and data …

Complex Question Answering over Knowledge Graph (C-KGQA) seeks to solve complex
questions using knowledge graphs. Currently, KGQA systems achieve great success in …

Background As global populations age and become susceptible to neurodegenerative
illnesses, new therapies for Alzheimer disease (AD) are urgently needed. Existing data …

In knowledge graph representation learning, link prediction is among the most popular and
influential tasks. Its surge in popularity has resulted in a panoply of orthogonal embedding …

With the continuous emergence of new knowledge, Knowledge Graph (KG) typically suffers
from the incompleteness problem, hindering the performance of downstream applications …

Abstract Knowledge graph completion (KGC) tasks are aimed to reason out missing facts in
a knowledge graph. However, knowledge often evolves over time, and static knowledge …

Knowledge-enhanced neural machine reasoning has garnered significant attention as a
cutting-edge yet challenging research area with numerous practical applications. Over the …

Link prediction for knowledge graphs (KGs), which aims to predict missing facts, has been
broadly studied in binary relational KGs. However, real world data contains a large number …

The application of cybersecurity knowledge graphs is attracting increasing attention.
However, many cybersecurity knowledge graphs are incomplete due to the sparsity of …

Skeleton-based action recognition depends on skeleton sequences to detect categories of
human actions. In skeleton-based action recognition, the recognition of action scenes with …