为生物医学知识图谱提供实时更新的嵌入服务，助力高效科研。
Bio-KGvec2go: Serving up-to-date Dynamic Biomedical Knowledge Graph Embeddings
- 基于KGvec2go API扩展，动态生成生物医学本体嵌入。
- 支持随本体版本发布自动更新，确保嵌入时效性。
- 用户无需计算资源即可获取最新嵌入，适合研究者快速应用。
知识图谱和本体以结构化方式表示实体及其关系，在现代AI应用开发中日益重要。将这些语义资源与机器学习模型结合，常依赖知识图谱嵌入模型将图数据转化为数值表示。因此，针对常用知识图谱和本体的预训练模型愈发珍贵，可避免不同任务重复训练同一数据，推动AI民主化并实现可持续计算。本文提出Bio-KGvec2go，是KGvec2go Web API的扩展，旨在为广泛使用的生物医学本体生成并提供知识图谱嵌入。鉴于这些本体具有动态特性，Bio-KGvec2go还支持与本体版本发布同步的定期更新。通过向用户提供无需额外计算开销的最新嵌入，该服务促进了生物医学研究的高效与及时开展。
原文摘要 · Abstract (English)
Knowledge graphs and ontologies represent entities and their relationships in a structured way, having gained significance in the development of modern AI applications. Integrating these semantic resources with machine learning models often relies on knowledge graph embedding models to transform graph data into numerical representations. Therefore, pre-trained models for popular knowledge graphs and ontologies are increasingly valuable, as they spare the need to retrain models for different tasks using the same data, thereby helping to democratize AI development and enabling sustainable computing. In this paper, we present Bio-KGvec2go, an extension of the KGvec2go Web API, designed to generate and serve knowledge graph embeddings for widely used biomedical ontologies. Given the dynamic nature of these ontologies, Bio-KGvec2go also supports regular updates aligned with ontology version releases. By offering up-to-date embeddings with minimal computational effort required from users, Bio-KGvec2go facilitates efficient and timely biomedical research.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。