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Applied temporal RDF: efficient temporal querying of RDF data with SPARQL


Tappolet, J; Bernstein, A (2009). Applied temporal RDF: efficient temporal querying of RDF data with SPARQL. In: 6th European Semantic Web Conference (ESWC), Crete, Greece, June 2009 - June 2009.

Abstract

Many applications operate on time-sensitive data. Some of
these data are only valid for certain intervals (e.g., job-assignments, versions of software code), others describe temporal events that happened at certain points in time (e.g., a persons birthday). Until recently, the only way to incorporate time into Semantic Web models was as a data type property. Temporal RDF, however, considers time as an additional dimension in data preserving the semantics of time.
In this paper we present a syntax and storage format based on named graphs to express temporal RDF. Given the restriction to preexisting RDF-syntax, our approach can perform any temporal query using standard SPARQL syntax only. For convenience, we introduce a shorthand format called t-SPARQL for temporal queries and show how t-SPARQL
queries can be translated to standard SPARQL. Additionally, we show that, depending on the underlying data’s nature, the temporal RDF approach vastly reduces the number of triples by eliminating redundancies resulting in an increased performance for processing and querying. Last but not least, we introduce a new indexing approach method that can significantly reduce the time needed to execute time point queries (e.g., what happened on January 1st).

Abstract

Many applications operate on time-sensitive data. Some of
these data are only valid for certain intervals (e.g., job-assignments, versions of software code), others describe temporal events that happened at certain points in time (e.g., a persons birthday). Until recently, the only way to incorporate time into Semantic Web models was as a data type property. Temporal RDF, however, considers time as an additional dimension in data preserving the semantics of time.
In this paper we present a syntax and storage format based on named graphs to express temporal RDF. Given the restriction to preexisting RDF-syntax, our approach can perform any temporal query using standard SPARQL syntax only. For convenience, we introduce a shorthand format called t-SPARQL for temporal queries and show how t-SPARQL
queries can be translated to standard SPARQL. Additionally, we show that, depending on the underlying data’s nature, the temporal RDF approach vastly reduces the number of triples by eliminating redundancies resulting in an increased performance for processing and querying. Last but not least, we introduce a new indexing approach method that can significantly reduce the time needed to execute time point queries (e.g., what happened on January 1st).

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30 citations in Web of Science®
65 citations in Scopus®
Google Scholar™

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496 downloads since deposited on 04 Feb 2010
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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Event End Date:June 2009
Deposited On:04 Feb 2010 11:14
Last Modified:06 Dec 2017 22:34

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