An Ontology-Driven Knowledge Graph for Basketball Box Scores: Semantic Filtering and LLM-Based Querying

This paper presents how an ontology-based Knowledge Graph (KG) for basketball box scores can be exploited to support several real use cases, and also presents competency questions, including sports analytics, complex question answering and data browsing with semantic filters. To illustrate this, we present the BBall ontology, the modeling decisions and the key advantages of creating a KG based on this ontology. Then, we introduce a KG covering 25 seasons of the EuroLeague and more than 5 million triples, and we showcase the functionality of three research prototypes based on that KG, particularly a faceted search application with semantic filters and two text-to-SPARQL applications leveraging LLMs, including support for multilingual queries. The first LLM-based application enables SPARQL query editing, and the second is a chat-based application offering interactive dialogue between the user and the system. For these applications, we describe their functionality and approach, and we compare them (along with a classical SPARQL query editor) in several dimensions. Finally, we provide the statistics for the constructed KG, indicative SPARQL queries addressing the competency questions, results and error analysis for the text-to-SPARQL method, efficiency results, and a historical analysis showing the evolution of several factors of EuroLeague basketball from 2000 to 2025.