Application of Hierarchical Attentive Relational Network in Diagnostic Prediction In recent years, Electronic Health Records (EHR) have become extremely valuable in improving medical decision-making, online disease detection, and monitoring. At the same time, deep learning methods have also achieved great success in utilizing EHR for health risk pr...

Temporal Aggregation and Propagation Graph Neural Networks (TAP-GNN) Background Introduction A temporal graph is a graph structure with dynamic interactions between nodes over continuous time, where the topology evolves over time. Such dynamic changes enable nodes to exhibit varying preferences at different times, which is critical for capturing us...

AutoAlign: A Fully Automated and Efficient Knowledge Graph Alignment Method Driven by Large Language Models Knowledge Graphs (KG) have been widely applied in fields such as question-answering systems, dialogue systems, and recommendation systems. However, different Knowledge Graphs often store the same real-world entities in various forms, leading ...

Knowledge Graph-Based Socially Enhanced Explainable Recommendation System Introduction With the increasing amount of information in the Internet world, the relevant information about users and products has rapidly expanded, leading to a growing problem of information overload. Recommendation systems can effectively alleviate this problem by recomme...

Knowledge Enhanced Graph Neural Networks for Explainable Recommendation Introduction With the explosive growth of online information, recommendation systems play an essential role in solving the problem of information overload. Traditional recommendation systems typically rely on Collaborative Filtering (CF) methods, which generate recommendations ...

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...

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...