Saturday 15 March 2025
A new approach to completing knowledge graphs, which are complex networks of interconnected concepts and relationships, has been developed by researchers. The method, called Diffusion-based Hierarchical Negative Sampling (DHNS), uses a combination of machine learning techniques and diffusion models to generate high-quality negative triples that can be used to train knowledge graph embedding models.
Knowledge graphs are widely used in artificial intelligence and natural language processing applications, such as question answering systems and recommender systems. However, they often contain incomplete or inaccurate information, which can limit their effectiveness. One way to address this issue is by using a technique called negative sampling, which involves generating synthetic negative triples that contrast with the positive triples in the knowledge graph.
DHNS uses a diffusion model to generate hierarchical embeddings of entities and relations in the knowledge graph. The model is trained on a large dataset of text and image modalities, which are used to capture the semantic relationships between entities and relations. The generated embeddings are then used to generate negative triples that are similar to the positive triples in the knowledge graph.
The DHNS approach has several advantages over traditional methods for generating negative triples. For example, it can handle large-scale knowledge graphs with millions of entities and relations, and it can generate high-quality negative triples that are more diverse and accurate than those generated by other methods.
In addition, DHNS is a highly scalable method that can be used to complete large-scale knowledge graphs in a short amount of time. This makes it a promising approach for applications where speed and efficiency are critical, such as real-time question answering systems.
The researchers evaluated the effectiveness of DHNS using three different datasets, including DB15K, MKG-W, and MKG-Y. The results showed that DHNS outperformed other state-of-the-art methods in terms of performance metrics such as mean reciprocal rank (MRR) and hit ratio (H1, H3, H10).
The authors also conducted an ablation study to evaluate the importance of each component of the DHNS approach. The results showed that the diffusion model was the most important component, followed by the hierarchical embedding generation module.
Overall, the DHNS approach is a promising new method for completing knowledge graphs and generating high-quality negative triples. Its ability to handle large-scale datasets and generate diverse and accurate negative triples makes it a valuable tool for applications where speed and accuracy are critical.
Cite this article: “Diffusion-Based Hierarchical Negative Sampling for Knowledge Graph Completion”, The Science Archive, 2025.
Knowledge Graphs, Diffusion Models, Negative Sampling, Machine Learning, Hierarchical Embeddings, Entity Relations, Natural Language Processing, Question Answering Systems, Recommender Systems, Scalability.
