Proverbia Semantic Engine: An OWL-Based Knowledge Graph for Semantic Querying of Cultural Proverbs

This preprint presents Proverbia Semantic Engine, a Semantic Web application that models 500 culturally annotated proverbs as a formal OWL ontology serialized in RDF/Turtle format. The system demonstrates an end-to-end pipeline: a curated CSV dataset is transformed into a machine-readable knowledge graph via an automated Python pipeline using rdflib and pandas, then exposed through a SPARQL query engine accessible via an interactive Streamlit web application. The ontology defines five OWL classes (Proverb, Theme, SubTheme, Region, Source) connected by object and datatype properties, enabling multi-dimensional semantic filtering across thematic, geographic, and source-based dimensions. Users can perform free-text keyword searches and complex filtering without modifying application code. This work demonstrates the practical application of knowledge representation and reasoning techniques to culturally grounded linguistic data. The project contributes both a reusable domain ontology and an open-source, deployable platform for proverb research and exploration. Live Application: https://proverbia.streamlit.appSource Code & Dataset: https://github.com/wirdan-1/Proverbia-Semantic-Engine