Master's thesis -Automated Generation and Validation of JSON-LD for Semantic Integration

Job description

In modern and increasingly decentralized data ecosystems, the automated integration of heterogeneous systems plays a central role. The goal of this thesis is to automatically generate semantically enriched JSON‑LD from existing JSON‑based API endpoints, leveraging ontology recommendations and domain-specific knowledge models. This should significantly reduce manual modeling effort and enable simple, interoperable data exchange. The work contributes to the digitalization of the energy sector by applying semantic methods to interconnect previously isolated data sources. This enables new value creation, e.g., through more efficient data usage, automated analyses, and improved collaboration between actors. In the long term, it supports the energy transition, as digital interoperability is a key requirement for flexibility, transparency, and the integration of modern energy generation units and storage systems. Be part of change: Automatic derivation of JSON‑LD contexts and structures from JSON data Use of ontology rankings to generate semantically correct types and properties Validation using SHACL, SPARQL, or reasoning Prototypical implementation of a pipeline for transforming API dat...