Deep Graph Memory Networks for Forgetting-Robust Knowledge Tracing

Deep Graph Memory Networks for Forgetting-Robust Knowledge Tracing

Deep Graph Memory Network for Forgetting-Robust Knowledge Tracing In recent years, Knowledge Tracing (KT) has attracted widespread attention as an important method for personalized learning. The goal of KT is to predict the accuracy of a student’s answers to new questions by utilizing their past answer history to estimate their knowledge state. How...

Connecting Embeddings Based on Multiplex Relational Graph Attention Networks for Knowledge Graph Entity Typing

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 ...

Unifying Large Language Models and Knowledge Graphs: A Roadmap

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 Non-Sampling for Knowledge Graph Enhanced Recommendation

Graph-Based Non-Sampling for Knowledge Graph Enhanced Recommendation

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...

Contextualized Graph Attention Network for Recommendation with Item Knowledge Graph

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...

Knowledge Graph Completion by Jointly Learning Structural Features and Soft Logical Rules

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...

Deep Relational Graph Infomax for Knowledge Graph Completion

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...

The Role of EEG Microstates in Predicting Oxcarbazepine Treatment Outcomes in Patients with Newly-Diagnosed Focal Epilepsy

The Role of EEG Microstates in Predicting Oxcarbazepine Treatment Outcomes in Patients with Newly-Diagnosed Focal Epilepsy

The Role of EEG Microstates in Predicting the Therapeutic Outcomes of Oxcarbazepine in Newly Diagnosed Focal Epilepsy Patients Introduction Background Focal epilepsy is the most common type of epilepsy, accounting for about 60% of all epilepsy cases. The selection of antiepileptic drugs (AEDs) varies depending on the type of epilepsy. In the treatm...

Multi-Level Feature Exploration and Fusion Network for Prediction of IDH Status in Gliomas from MRI

Multi-Level Feature Exploration and Fusion Network for Prediction of IDH Status in MRI Background Glioma is the most common malignant primary brain tumor in adults. According to the 2021 World Health Organization (WHO) classification of tumors, genotype plays a significant role in the classification of tumor subtypes, especially the isocitrate dehy...

A Siamese-Transport Domain Adaptation Framework for 3D MRI Classification of Gliomas and Alzheimer’s Diseases

Classification of 3D MRI Gliomas and Alzheimer’s Disease Based on the Siamese-Transport Domain Adaptation Framework Background In computer-aided diagnosis, 3D magnetic resonance imaging (MRI) screening plays a vital role in the early diagnosis of various brain diseases, effectively preventing the deterioration of the condition. Glioma is a common m...