Affiliations: [a] School of Information, Liaoning University, Shenyang, China
| [b] School of Innovation and Entrepreneurship, Liaoning University, Shenyang, China
Correspondence:
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Corresponding author. Wei Yan, School of Information, School of Innovation and Entrepreneurship, Liaoning University, Shenyang 110036, China. E-mail: [email protected].
Abstract: With the rapid development of Semantic Web, the retrieval of RDF data has become a research hotspot. As the main method of data retrieval, keyword search has attracted much attention because of its simple operation. The existing RDF keyword search methods mainly search directly on RDF graph, which is no longer applicable to RDF knowledge graph. Firstly, we propose to transform RDF knowledge graph data into type graph to prune the search space. Then based on type graph, we extract frequent search patterns and establish a list from frequent search patterns to pattern instances. Finally, we propose a method of the Bloom coding, which can be used to quickly judge whether the information our need is in frequent search patterns. The experiments show that our approach outperforms the state-of-the-art methods on both accuracy and response time.
