Semantic Vector-Based Query Extension for RDF Graphs

This paper describes an efficient framework for querying RDF (Resource Description Framework) graphs, which contain billions of labeled entities, using simplified SPARQL queries. Due to the schema-free nature of RDF data, it is challenging for users to understand the underlying structure and create complex queries. The paper proposes a solution that extends simplified queries using knowledge semantics to retrieve approximate answers. The framework mines RDF graphs for semantically equivalent patterns, known as topic graphs, by using large language model (LLM) embeddings to generate semantic vectors. It then constructs approximate queries to retrieve top-k results based on semantic similarity. Extensive tests on the DBpedia dataset and QALD-4 benchmark demonstrate the effectiveness and efficiency of the approach.