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. |
