Semantic Extension of Query for the Linked Data

Semantic Extension of Query for the Linked Data

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) and Zhilei Yin (School of Computer Science, Northwestern Polytechnical University, Xi′an, China)
Copyright: © 2017 |Pages: 25
DOI: 10.4018/IJSWIS.2017100106
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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.
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The goal of Linked Data query processing is an online execution of declarative queries over the SW, by relying only on the Linked Data principles (Bizer et al., 2009). Aiming to unleash the potential of SW, a number of general methods for querying Linked Data have been developed.

Early querying methods like Sindice (Oren et al., 2008) and Falcons (Cheng & Qu, 2009) are based on keywords. Since SPARQL (Harris & Seaborne, 2010) can be used to express queries across diverse data sources, it becomes the foundation of many recent researches about Linked Data querying. The devices in (Umbrich et al., 2011; Wagner et al., 2012) use index-based source selection and provide source ranking, while the techniques mentioned in (Hartig, 2013; Miranker et al., 2012) are traversal-based query execution approaches without source ranking. Moreover, some domain-oriented or datasource-oriented methods are presented to further pursue the accuracy and efficiency. NAGA (Mahdisoltani et al., 2014) provides best-effort heuristics to return and rank the relevant RDF triples from YAGO (Hoffart et al., 2013). By using SPARQL and SPIN, Lo Bue and Machi (Lo Bue & Machi, 2015) study on integrating and querying over tourism domain datasets via interlinking techniques. MEQLD (Tran & Nguyen, 2015) investigates to improve the mapping extension of lexical entities into DBpedia’s components for creating query in SPARQL. Besides, other current works focus on mapping visual method to SPARQL (Haag et al., 2015), optimizing query results over several heterogeneous Linked Data sources (Taelman, 2016), as well as automatically generating SPARQL query (Alec et al., 2016).

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