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

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 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).

Streaming the Web: Reasoning over dynamic data

“…Similar approaches for including time into RDF, as well as proposals for time and context-aware query languages, have been explored in [101][102][103][104][105][106][107]. All these solutions lack the support for continuous queries.…”

Linked Data and Time – Modeling Researcher Life Lines by Events

“…Within the database community, extensive research has been conducted on two notions of time: valid time (when a change occurred in the real world) and transaction time (when a change was entered to the database) [22]. Various proposals adapting the notion of valid time have been made by the Linked Data community, such as temporal RDF graphs (temporal reification vocabulary) [16,15], multidimensional RDF (extended triple notion) [10], applied temporal RDF (named graphs) [43], stRDF (temporal quad) [25], RDF SpatialTemporalThematic (based on temporal graphs) [34], and temporal quintuples [26].…”

Representing and Querying Validity Time in RDF and OWL: A Logic-Based Approach

“…RDF(S) and OWL 2 can represent only static facts whose truth does not change with time. This deficiency has been recognized in the Semantic Web community, and a number of approaches for the representation, reasoning, and querying of validity time were developed [22,23,24,25,26,27,28,29]; we discuss various aspects of these approaches in Section 7. These approaches, however, do not provide a general framework for the management of validity time in the Semantic Web: all approaches known to us are applicable only to entailment relations that can be characterized by deterministic inference rules without existential quantifiers, such as RDFS or OWL 2 RL.…”