Paper Info
Title
Semantic Extension of Query for the Linked Data
Abstract
AbstractWith 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.
Year
DOI
Venue
2017
10.4018/IJSWIS.2017100106
Periodicals
Keywords
Field
DocType
Big Data, Linked Data, Query Model, Semantic Extension, SPARQL
Query optimization,Data mining,RDF query language,Query language,Information retrieval,Semantic Web Stack,Query expansion,Computer science,Sargable,Web query classification,Semantic computing
Journal
Volume
Issue
ISSN
13
4
1552-6283
Citations 
PageRank 
References 
3
0.37
22
Authors
4
Name
Order
Citations
PageRank
Pu Li19615.13
Yuncheng Jiang265.13
Ju Wang317212.45
Zhilei Yin430.70