Using Connection Embeddings Based on Multi-Relational Graph Attention Networks for Entity Typing in Knowledge Graphs Research Background Today, knowledge graphs (KGs) are garnering increasing research interest in various AI-related fields driven by KGs. Large-scale knowledge graphs provide rich and efficient structured information, serving as core ...

Unified Large Language Models and Knowledge Graphs Background In recent years, numerous research achievements have emerged in the fields of natural language processing and artificial intelligence. Notably, large language models (LLMs) such as ChatGPT and GPT-4 have demonstrated remarkable performance. However, despite their excellent generalization...

Graph-Based Sampling-Free Knowledge Graph Enhanced Recommendation In recent years, knowledge graph (KG) enhanced recommendation systems, aiming to address cold start problems and the interpretability of recommendation systems, have garnered substantial research interest. Existing recommendation systems typically focus on implicit feedback such as p...

Knowledge Graph-based Recommendation System: Contextualized Graph Attention Network In recent years, with the explosive growth of online information and content, recommendation systems have become increasingly important in various scenarios such as e-commerce websites and social media platforms. These systems typically aim to provide users with a l...

In recent years, Knowledge Graphs (KG) have been widely used in many artificial intelligence tasks. Knowledge graphs represent entities and their relationships using triplets consisting of a head entity, a relation, and a tail entity. For example, the triplet (h = Paris, r = capital_of, t = France) represents a common-sense fact about the real worl...

Knowledge Graph (KG) embedding technology is an important research topic in the field of artificial intelligence, mainly used for knowledge acquisition and extension of knowledge graphs. In recent years, although many graph embedding methods have been proposed, these methods typically focus only on the semantic information of the knowledge graph, i...

Graph-Based Conditional Generative Adversarial Network for Generating Synthetic Functional Brain Networks to Diagnose Major Depressive Disorder Research Background: Major Depressive Disorder (MDD) is a widespread mental disorder that affects millions of people’s lives and poses a significant threat to global health. Studies have shown that function...

Evaluation of Efficacy of Epilepsy Surgery for Dominant Mesial Temporal Lobe Epilepsy without Hippocampal Sclerosis Original Research | Journal of Clinical Neuroscience 111 (2023) 16-21 Introduction Approximately 0.5%-1% of the global population suffers from epilepsy (Fiest et al. 2017), with about 30% of these patients being refractory to medicati...

Thalamocortical Connectivity Reduction in Rolandic Epilepsy Rolandic Epilepsy (RE), also known as self-limited epilepsy with centrotemporal spikes (SELECTS), is the most common localized developmental epileptic encephalopathy. This type of epilepsy is typically accompanied by transient mild to severe cognitive symptoms, sleep-related rolandic spike...

Changes in Functional Connectivity in Mild Cognitive Impairment: A Meta-Analysis of M/EEG Studies Background and Objectives Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by memory loss and cognitive impairment. AD is the leading cause of cognitive disorders in the elderly, accounting for approximately 60% to 80% of global c...