tools4RDF is a lightweight Python framework designed to simplify working with RDF-based ontologies and data models. It allows one or more ontologies to be parsed and represented as Python classes, making it easier to navigate and explore their structure through features like autocompletion in interactive environments such as Jupyter notebooks. The framework preserves key semantic details such as domain and range information, which can then be used programmatically within Python workflows. A central goal of the tool is to make querying knowledge graphs with SPARQL more accessible to users without deep expertise in semantic web technologies. This is achieved through a programmatic interface that abstracts away much of the complexity involved in writing SPARQL queries. The overall workflow of the approach is illustrated in Figure 1, showing how tools4RDF parses ontologies, constructs a network representation, and generates SPARQL queries to retrieve data from knowledge graphs. This design lowers the barrier to entry for domain scientists and developers unfamiliar with RDF, while still exposing enough control for advanced use cases. Originally developed for ontology-driven data integration in materials science, tools4RDF is ontology-agnostic and can be used across a wide range of scientific domains.
Menon et al. (Thu,) studied this question.