An Explainable and Personalized Cognitive Reasoning Model Based on Knowledge Graph: Toward Decision Making for General Practice Background General medicine, as an important part of community and family healthcare, covers different ages, genders, organ systems, and various diseases. Its core concept is human-centered, family-based, emphasizing long-...

Application of Knowledge-Enhanced Graph Topic Transformer in Interpretable Biomedical Text Summarization Research Background Due to the continuous increase in the volume of biomedical literature, the task of automatic biomedical text summarization has become increasingly important. In 2021 alone, 1,767,637 articles were published in the PubMed data...

Multiple Prior Knowledge Graph Neural Network in Multi-Omics Data Analysis Background Introduction Precision medicine is an important field for the future of healthcare as it provides personalized treatment plans for patients, improving treatment outcomes and reducing costs. For instance, due to the complex clinical, pathological, and molecular cha...

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 ...