Temporal Ontology Language for Representing and Reasoning Interval-Based Temporal Knowledge

Kim, Sang-Kyun; Song, Mi‐Young; Kim, Chul; Yea, Sang-Jun; Jang, Hyun Chul; Lee, Kyu-Chul

doi:10.1007/978-3-540-89704-0_3

Cited by 14 publications

(2 citation statements)

References 4 publications

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“…For example, Artale and Franconi [5] propose to extend Description Logics with temporal features, while Welty and Fikes [6], Kim et al [7] and the tOWL [8] project aim at introducing temporal entities into OWL. O'Connor et al [9] investigated the querying of OWL data from the medical domain.…”

Section: Temporal Extensionsmentioning

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: Temporal Extensionsmentioning

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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“…Quite a number of papers, particularly [19], highlight the importance of time in the semantic web by fully expressing most temporal concepts [2], [6], [17][18][19], [24][25], [31], [34], [35], [42]. Based on these papers, it is clear that a gap exists in describing temporal query languages.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Time Concept Modeling for Querying Temporal Information in OWL and RDF

Bahadorani¹,

Zaeri²

2017

IJITCS

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Abstract-Ontology is an important factor in the integration of heterogeneous semantic information. Description logic, as a formal language for expressing ontologies, does not include the necessary features to create a temporal dimension in the relationships among concepts. It is critical to introduce time concepts to model temporal data and relate them to other non-temporal data recorded in ontology. Current query languages in the semantic web are not able to respond to temporal questions; thus, another important issue is to have the appropriate methods for answering temporal questions. In this paper, temporal modeling methods in OWL and RDF are assessed and the temporal query languages for expressing queries in the semantic web are categorized and compared.Index Terms-Ontology, temporal model, description logic, temporal query language.

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