Semantic Extension of Query for the Linked Data

Pu Li, Yuncheng Jiang, Ju Wang and Zhilei Yin
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Pu Li: Software Engineering College, Zhengzhou University of Light Industry, Zhengzhou, China & School of Computer Science, South China Normal University, Guangzhou, China
Yuncheng Jiang: School of Computer Science, South China Normal University, Guangzhou, China
Ju Wang: College of Computer Science and Information Engineering, Guangxi Normal University, Guilin, China
Zhilei Yin: School of Computer Science, Northwestern Polytechnical University, Xi′an, China

International Journal on Semantic Web and Information Systems (IJSWIS), 2017, vol. 13, issue 4, 109-133

Abstract: With the advent of Big Data Era, users prefer to get knowledge rather than pages from Web. Linked Data, a new form of knowledge representation and publishing described by RDF, can provide a more precise and comprehensible semantic structure to satisfy the aforementioned requirement. Further, the SPARQL query language for RDF is the foundation of many current researches about Linked Data querying. However, these SPARQL-based methods cannot fully express the semantics of the query, so they cannot unleash the potential of Linked Data. To fill this gap, this paper designs a new querying method which extends the SPARQL pattern. Firstly, the authors present some new semantic properties for predicates in RDF triples and design a Semantic Matrix for Predicates (SMP). They then establish a well-defined framework for the notion of Semantically-Extended Query Model for the Linked Data (SEQMLD). Moreover, the authors propose some novel algorithms for executing queries by integrating semantic extension into SPARQL pattern. Lastly, experimental results show that the authors' proposal has a good generality and performs better than some of the most representative similarity search methods.

Date: 2017
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