# Graph Neural Network-Based Entity Extraction and Relationship Reasoning in Complex Knowledge Graphs

arXiv: https://arxiv.org/abs/2411.15195 · HTML: https://mlsanity.com/paper/2411.15195/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 19 references that are in the map.
- First posted on arXiv 19 Nov 2024.

## Builds on (most cited first, 12 of 12)

- [Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining](https://mlsanity.com/paper/2409.14327/) (2024)
- [Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks](https://mlsanity.com/paper/2410.17617/) (2024)
- [Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generation](https://mlsanity.com/paper/2411.03572/) (2024)
- [Emotion-Aware Interaction Design in Intelligent User Interface Using Multi-Modal Deep Learning](https://mlsanity.com/paper/2411.06326/) (2024)
- [Metric Learning for Tag Recommendation: Tackling Data Sparsity and Cold Start Issues](https://mlsanity.com/paper/2411.06374/) (2024)
- [A Lightweight GAN-Based Image Fusion Algorithm for Visible and Infrared Images](https://mlsanity.com/paper/2409.15332/) (2024)
- [Efficient and Aesthetic UI Design with a Deep Learning-Based Interface Generation Tree Algorithm](https://mlsanity.com/paper/2410.17586/) (2024)
- [Contrastive Learning for Knowledge-Based Question Generation in Large Language Models](https://mlsanity.com/paper/2409.13994/) (2024)
- [A Recommendation Model Utilizing Separation Embedding and Self-Attention for Feature Mining](https://mlsanity.com/paper/2410.15026/) (2024)
- [Optimizing News Text Classification with Bi-LSTM and Attention Mechanism for Efficient Data Processing](https://mlsanity.com/paper/2409.15576/) (2024)
- [Automated Genre-Aware Article Scoring and Feedback Using Large Language Models](https://mlsanity.com/paper/2410.14165/) (2024)
- [Reinforcement Learning for Adaptive Resource Scheduling in Complex System Environments](https://mlsanity.com/paper/2411.05346/) (2024)

## Built on by (most cited first, 9 of 9)

- [Adaptive Optimization for Enhanced Efficiency in Large-Scale Language Model Training](https://mlsanity.com/paper/2412.04718/) (2024)
- [Machine Learning Techniques for Pattern Recognition in High-Dimensional Data Mining](https://mlsanity.com/paper/2412.15593/) (2024)
- [AI-Driven Health Monitoring of Distributed Computing Architecture: Insights from XGBoost and SHAP](https://mlsanity.com/paper/2501.14745/) (2024)
- [Advanced Risk Prediction and Stability Assessment of Banks Using Time Series Transformer Models](https://mlsanity.com/paper/2412.03606/) (2024)
- [Dynamic Scheduling Strategies for Resource Optimization in Computing Environments](https://mlsanity.com/paper/2412.17301/) (2024)
- [Optimizing Multi-Task Learning for Enhanced Performance in Large Language Models](https://mlsanity.com/paper/2412.06249/) (2024)
- [Collaborative Optimization in Financial Data Mining Through Deep Learning and ResNeXt](https://mlsanity.com/paper/2412.17314/) (2024)
- [Deep Learning in Image Classification: Evaluating VGG19's Performance on Complex Visual Data](https://mlsanity.com/paper/2412.20345/) (2024)
- [Dynamic User Interface Generation for Enhanced Human-Computer Interaction Using Variational Autoencoders](https://mlsanity.com/paper/2412.14521/) (2024)

## Abstract (arXiv)

This study proposed a knowledge graph entity extraction and relationship reasoning algorithm based on a graph neural network, using a graph convolutional network and graph attention network to model the complex structure in the knowledge graph. By building an end-to-end joint model, this paper achieves efficient recognition and reasoning of entities and relationships. In the experiment, this paper compared the model with a variety of deep learning algorithms and verified its superiority through indicators such as AUC, recall rate, precision rate, and F1 value. The experimental results show that the model proposed in this paper performs well in all indicators, especially in complex knowledge graphs, it has stronger generalization ability and stability. This provides strong support for further research on knowledge graphs and also demonstrates the application potential of graph neural networks in entity extraction and relationship reasoning.