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Related Concept Videos

Anatomical Terminology01:20

Anatomical Terminology

Knowledge of anatomy is essential to understand human biology and medicine. Anatomists and health care professionals use standard terminology to describe the human body with more precision and no ambiguity. Anatomical terms have mostly Greek and Latin-derived roots. Because these languages are rarely used in conversation, the meaning of words remains the same. Each term is made up of a root in between the prefixes and suffixes. The root of a term often refers to an organ, tissue, or condition,...
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Rapid Setup of Tissue Microarray and Tiled Area Imaging on the Multiplexed Ion Beam Imaging Microscope Using the Tile/SED/Array Interface
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Rapid Setup of Tissue Microarray and Tiled Area Imaging on the Multiplexed Ion Beam Imaging Microscope Using the Tile/SED/Array Interface

Published on: September 15, 2023

tOWL: a temporal Web Ontology Language.

Viorel Milea1, Flavius Frasincar, Uzay Kaymak

  • 1Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, 3000 DR Rotterdam, The Netherlands. milea@ese.eur.nl

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|September 1, 2011
PubMed
Summary
This summary is machine-generated.

This paper introduces temporal OWL, a new language for the Semantic Web. It enables the representation of time-dependent information in ontologies, crucial for intelligent systems.

Related Experiment Videos

Last Updated: May 29, 2026

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Knowledge Representation

Background:

  • The Semantic Web and Web Ontology Language (OWL) offer powerful reasoning capabilities.
  • A lack of standard methods for temporal information in OWL hinders intelligent system development.

Purpose of the Study:

  • To present a temporal extension of OWL, named temporal OWL.
  • To enable the standard representation of time and time-dependent data within OWL ontologies.

Main Methods:

  • Introduced a temporal extension to the SHIN(D) fragment of OWL.
  • Developed a layered approach with three key extensions: concrete domains, temporal representation (time points, intervals, Allen's relations), and timeslices/fluents.
  • Implemented a perdurantist view for individuals to model temporal aspects like state transitions.

Main Results:

  • Developed a novel temporal OWL language.
  • Demonstrated the language's capability to represent complex temporal aspects, including process state transitions.
  • Illustrated the language's expressiveness with a financial domain example.

Conclusions:

  • Temporal OWL addresses the need for standard temporal representation in OWL.
  • The proposed language enhances the Semantic Web's potential for intelligent systems by incorporating temporal reasoning.
  • This extension facilitates richer modeling of dynamic and time-varying information in ontologies.