Bio-KGvec2go: Serving up-to-date Dynamic Biomedical Knowledge Graph Embeddings

2025-09-09

AI Summary

Biomedical ontology embeddings suffer from outdated representations, high computational overhead, and insufficient support for time-sensitive AI applications. Method: This paper introduces the first ontology-aware automated continual embedding update framework. Extending KGvec2go, it integrates knowledge graph embedding techniques with semantic resources to generate lightweight vector representations capturing ontology version evolution; multi-version embeddings are delivered via a Web API, enabling zero-shot deployment without retraining. Contribution/Results: We present the first fully automated incremental update mechanism for biomedical ontology embeddings, reducing redundant training costs by over 90% in empirical evaluation. The framework significantly enhances cross-task model reusability and downstream application timeliness, establishing a plug-and-play, dynamic semantic infrastructure for life science AI.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Intelligent Robots: Embodied AI

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Providing up-to-date biomedical knowledge graph embeddings
• Supporting regular updates for dynamic biomedical ontologies
• Enabling efficient integration of semantic resources with AI

Innovation

• Dynamic biomedical knowledge graph embedding generation
• Regular updates aligned with ontology version releases
• Web API serving up-to-date embeddings efficiently