Introducing Time into RDF

Gutiérrez, Claudio; Hurtado, Claudia; Vaisman, Alejandro

doi:10.1109/tkde.2007.34

Cited by 225 publications

(237 citation statements)

References 15 publications

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“…Therefore, it needs to be regarded as an element of the meta model instead of being just part of the data model. By applying the foundations established by Gutierrez et al [1,2], our approach proposes to implement, first, an RDF [3] compatible syntax for temporal data. Second, we provide strategies for efficiently storing temporal RDF data (including an interval based index structure).…”

Section: Introductionmentioning

confidence: 99%

Applied Temporal RDF: Efficient Temporal Querying of RDF Data with SPARQL

Tappolet

Bernstein

2009

Lecture Notes in Computer Science

114

119

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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 person's 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 τ -SPARQL for temporal queries and show how τ -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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Section: Introductionmentioning

confidence: 99%

Applied Temporal RDF: Efficient Temporal Querying of RDF Data with SPARQL

Tappolet

Bernstein

2009

Lecture Notes in Computer Science

114

119

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“…Temporal RDF proposes an extension of the standard RDF for naming properties with the corresponding time interval, allowing explicit management of time. [5]. Reification is a technique used to represent n-ary relations with languages such as OWL which allow only binary relations [6] .…”

Section: Representing Spatial Dynamics In An Ontologymentioning

confidence: 99%

A Semantic Model to Query Spatial–Temporal Data

Harbelot

Arenas

Cruz

2013

Lecture Notes in Geoinformation and Cartography

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Abstract. There is a growing need for the study of spatial-temporal objects and their relationships. A common approach for this task is the use of relational databases, which unfortunately do not allow inference. In this research we introduce a new approach that uses the concept of "continuum" together with ontologies and semantic web technologies. The "continuum" allows us to define parent-child relationships between representations of objects. It also allows us to compare the evolution of two different objects and establish the relationships between them along time. Our approach is based on the 4D fluent, which is extended to obtain spatial-temporal qualitative information from the analysis of objects and their relationships. The results of our analysis are later added to our knowledge base enhancing it. Our preliminary results are promising and we plan to further develop the model in the near future.

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“…In [4], the authors present a framework to incorporate temporal reasoning into RDFS [5] [6]. The author associate RDF triples with their validity temporal interval and apply the RDFS inference rules (which are always valid).…”

Section: T C |= Max V(l:?i)mentioning

confidence: 99%

“…Like our work, their semantics is based on time points and not on temporal intervals. Yet, [4] does not consider strong and weak negation and validity intervals on logic rules. Additionally, it does not support simple queries of type 3 and type 4 and the filter condition is limited.…”

Section: T C |= Max V(l:?i)mentioning

confidence: 99%