基于语义度量的RDF图近似查询

Approximating Query with Semantic-Based Measure on RDF Graphs

Approximating query is one of the operations for resource management in graph database.Existing works mainly based on the distance similarity to measure the query and graphs without considering the semantic similarity.For the approximating query on semantic graphs,disregarding the semantic similarity may fail the query.In this paper,we propose a semantic-based measure for approximating query on RDF graphs,considering the semantic similarity.In the meanwhile,we specify a semantic structural pruning strategy to ameliorate the efficiency of query process.Finally,we construct the query framework to answer approximating query,and design experiments to test our methods under this framework.Results show that the approaches in this paper can efficiently execute approximating query on RDF graphs and effectively return top-k results.