Uncovering Hidden Connections – Developing Cross-Dataset Analytical Methods for Knowledge Graphs with Spatial Information

Description

The TRAIL addresses the key challenge of making the heterogeneous datasets integrated into the NFDI4Objects knowledge graph (N4O KG) usable for the research community beyond simple queries. The N4O KG brings together the diverse, spatially and semantically complex data from various consortium repositories into a shared knowledge base. To date, however, the use of this data has been largely limited to manually formulated, complex SPARQL queries that capture only explicitly modeled relations, thereby overlooking many potential contextual connections.

To close this methodological gap, the TRAIL is developing conceptual and exemplary cross-dataset analysis methods capable of identifying hidden structures, semantic patterns, and spatiotemporal relationships within the knowledge graph. In doing so, concepts from graph analysis, social network analysis, and the analysis of heterogeneous information networks are applied to the specific characteristics of (bio)archaeological data and adapted for use in the NFDI4Objects context.

The goal is to open up new data-driven perspectives and exploratory approaches for researchers without a technical background that go beyond the limits of traditional query languages. In the long term, TRAIL will thus create the methodological foundation for expandable analysis and exploration services within the N4O KG infrastructure.