Sprecher
Beschreibung
Knowledge graphs enable the structuring and linking of scientific metadata from heterogeneous sources, supporting advanced search and analysis. Their effectiveness, however, depends on the level of metadata consistency and standardization. Graphs linking high-quality topical information can also be very useful for training tailored LLMs and AI models for specific purposes. With this activity, we aim to identify and extract topical datasets from repositories, improve and harmonize discipline-specific information in the metadata, turn them into graphs, and make them available as training data for AI applications.
For this purpose, we explore an approach to building knowledge graphs from project metadata in scientific repositories. We use projects as a linking layer to integrate and align datasets, as well as their associated methods, instruments, keywords, and geographic descriptions. Metadata is extracted and normalized from structured fields and textual descriptions, and then subjected to semantic categorization.
The poster presents the methods applied, encountered challenges, and results, including statistics on harvested metadata, category coverage, and observed differences in metadata representation across repositories. We also discuss the potential of project-oriented knowledge graphs for improving the quality and interoperability of scientific metadata.