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UMP-ST Plug-in: Documenting, Maintaining and Evolving Probabilistic Ontologies Using UnBBayes Framework Read chapter Read first chapter

2014 | OriginalPaper | Chapter

A Metaontology for Annotating Ontology Entities with Vagueness Descriptions

Authors : Panos Alexopoulos, Silvio Peroni, Boris Villazón-Terrazas, Jeff Z. Pan, José Manuel Gómez-Pérez

Published in: Uncertainty Reasoning for the Semantic Web III

Publisher: Springer International Publishing

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Abstract

The emergence in the last years of initiatives like the Linked Open Data (LOD) has led to a significant increase in the amount of structured semantic data on the Web. Central role to this development has been played by ontologies, as these enable the representation of real world domains in an explicit and formal way and, thus, the production of commonly understood and shareable semantic data. Nevertheless, the shareability and wider reuse of such data can be hampered by the existence of vagueness within it, as this makes the data’s meaning less explicit. With that in mind, in this paper we present and evaluate the Vagueness Ontology, a metaontology that enables the explicit identification and description of vague entities and their vagueness-related characteristics in ontologies. The rationale is that such descriptions, when accompanying vague ontologies, may narrow the possible interpretations that the latter’s vague elements may assume by its users.

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previous chapter Semantics and Inference for Probabilistic Description Logics
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Footnotes
10
All the entities of the Vagueness Ontology are introduced in Manchester Syntax [15], while the examples of use of the ontology are presented in Turtle [27].
 
12
Available at http://esurv.org?u=vagueness-ontology.
 
14
Even if confidence intervals of the SUS scores will be rather wide (e.g., in our experiment we obtained [56.06, 78.45]), the average SUS score will be surprisingly stable even with a small sample. As stated in [29] and summarised in his blog (see http://​www.​measuringusabili​ty.​com/​blog/​10-things-SUS.​php for more details), Sauro “did several computer simulations and showed that [...] the mean from a sample size of just 5 repeated 1000 times [...] was within 6 points of the true SUS score” in the 50 % of the 1000 samples used – note that the true SUS score was calculated using the original big sample Sauro had available. This means that “you get within the ballpark of the actual SUS score in more than half of the cases with very small sample sizes” – e.g., “if the actual SUS score was a 74, average SUS scores from five users will fall between 68 and 80 half of the time”.
 
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Metadata
Title
A Metaontology for Annotating Ontology Entities with Vagueness Descriptions
Authors
Panos Alexopoulos
Silvio Peroni
Boris Villazón-Terrazas
Jeff Z. Pan
José Manuel Gómez-Pérez
Copyright Year
2014
Book
Uncertainty Reasoning for the Semantic Web III

Print ISBN: 978-3-319-13412-3

Electronic ISBN: 978-3-319-13413-0

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-319-13413-0_6

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