PUBLICATION
Deep Semantic Linking of Scientific Knowledge: An Agentic AI Framework for Knowledge Graph Construction
Type
Conference Paper
Year
2026
Authors
Sandra Schaftner M.Sc.
Prof. Dr.-Ing. Martin Gaedke
Research Area
Event
26th International Conference on Web Engineering
Published in
26th International Conference on Web Engineering, Lyon, France
ISBN/ISSN
tba
Download
Abstract
The foundation of scientific research is the comprehensive analysis of existing literature. However, the exponential growth of published research leaves scientists increasingly overwhelmed, making it difficult to maintain a complete overview of the state-of-the-art or to discover hidden synergies between studies. The root of this problem lies in the traditional format of scholarly communication: crucial knowledge about applied methods, datasets, and metrics remains locked in semantically unlinked documents. While this format is optimal for human reading, it is highly inefficient for machine processing. Even modern AI research assistants frequently fail to provide complete, verifiable, and hallucination-free answers to complex research queries. To enable true machine-assisted exploration and verification, scholarly literature must be transformed from isolated documents into deeply interlinked, machine-readable structures. To achieve this, we introduce an agentic AI framework that automatically extracts key research entities, seamlessly interlinking the literature by mapping them to standard knowledge bases via unified URIs.
Reference
TBA
