Knowledge Engineering in a Temporal Semantic Web Context

Milea, Viorel; Frăsincar, Flavius; Kaymak, Uzay

doi:10.1109/icwe.2008.10

Cited by 22 publications

(7 citation statements)

References 8 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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“…W pracy [Perry 2006] zaprezentowano przestrzenno-temporalny model danych, wykorzystujący te grafy. Jeśli chodzi o OWL to autorzy pracy [Milea, Frasincar, Kaymak 2008] przedstawili nowy formalizm temporalny bazują-cy na OWL (TOWL), zaś autorzy [Salguero, Araque, Delgado 2008] pokazali przestrzenno-temporalny OWL (STOWL).…”

Section: Reprezentacja Ontologii W Rdf I Owlunclassified

Wiedza i ontologie w temporalnych systemach inteligentnych

Mach‐Król¹

2015

Informatyka Ekonomiczna

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Streszczenie: W artykule przedstawiono kwestie temporalnego ujęcia wiedzy o dziedzinie ekonomicznej. Omówiono problematykę wiedzy temporalnej i inteligentnych systemów temporalnych, reprezentacji ontologii temporalnej oraz przedstawiono system PROTON − system wnioskujący dla temporalnych ontologii i baz wiedzy.Słowa kluczowe: wnioskowanie temporalne, czas, temporalne systemy inteligentne, ontologia, wiedza ekonomiczna, otoczenie ekonomiczne. Summary:The article presents a temporal point of view of knowledge on economics. It discusses temporal knowledge and temporal intelligent systems as well as temporal ontology representation. It also presents PROTON system -an inference system for temporal ontologies and knowledge bases.

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“…Nary relations [2] is considered to be an alternative to the 4-D fluents approach, although the 4-D fluents representation where the property is holding among two timeslices of objects and not between the two objects and the intermediate object representing their relation may seems more natural to users. TOWL [23] is a temporal representation approach based on 4-D fluents that extends OWL syntax with temporal concepts and supports quantitative time intervals.…”

Section: Background and Related Workmentioning

confidence: 99%

Representing Temporal Knowledge in the Semantic Web: The Extended 4D Fluents Approach

Batsakis

Petrakis

2011

Combinations of Intelligent Methods and Applications

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Abstract-Representing information that evolves in time in ontologies, as well as reasoning over static and dynamic ontologies are the areas of interest in this work. Building upon well established standards of the semantic Web and the 4D-fluents approach for representing the evolution of temporal information in ontologies, this work demonstrates how qualitative temporal relations that are common in natural language expressions (i.e., relations between time intervals like "before", "after", etc.) are represented in ontologies. Existing approaches allow for representations of temporal information, but do not support representation of qualitative relations and reasoning. I. INTRODUCTIONOntologies offer the means for representing high level concepts, their properties and their interrelationships. Dynamic ontologies will in addition enable representation of information evolving in time. In particular, dynamic ontologies are not only suitable for describing static scenes with static objects (e.g., objects in photographs) but also enable representation of events with objects and properties changing in time (e.g., moving objects in a video). Representation of both static and dynamic information in ontologies, as well as reasoning over static and dynamic ontologies are exactly the problems this work is dealing with.Representation of dynamic features calls for mechanisms allowing representation of the notion of time (and of properties varying in time) [ [9] with the last being the most efficient. All approaches suffer from data redundancy as several objects are created for each binary relationship changing in time (i.e., for each new event, a new temporal object and an additional binary relationship for each temporal property of this object is created and associated with existing classes) thus complicating the ontology. Also, adding a time argument to binary relationships may (as in reification and named graphs) complicate application of OWL language constructs (e.g., cardinality constraints, inverse, transitive relations are no longer applicable) thus limiting OWL expressivity and obstructing reasoning. The 4D fluents approach, still suffers from data redundancy but maintains OWL expressiveness and reasoning support (i.e., an OWL reasoner such as Pellet can still be applied to fully exploit OWL

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Knowledge Engineering in a Temporal Semantic Web Context

Cited by 22 publications

References 8 publications

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

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

Wiedza i ontologie w temporalnych systemach inteligentnych

Representing Temporal Knowledge in the Semantic Web: The Extended 4D Fluents Approach

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