Investigación

Publicación (Artículos en revistas científicas)

Uncertainty Quantification in Mathematics-Embedded Ontologies Using Stochastic Reduced Order Model

, ; , ; Eckert, Martina; ,
Resumen:
To resolve one of uncertainty features, randomness, in ontologies, this paper shows how to characterize uncertainty of concepts from a statistical viewpoint. In addition, with a focus on indirect entities, which are computed from direct entities through mathematical models, the uncertainties propagated from those direct entities are important and should be quantified. Thus, a novel algorithm, named Stochastic Reduced Order Model (SROM), is presented to be applied to quantify the ontological uncertainty propagation in presence of multiple input entities. This SROM-based method could approximate the statistics of indirect entities accurately and efficiently by using a very small amount of samples of input entities. The computational cost is considerably reduced while guaranteeing a reasonable degree of accuracy. Furthermore, the predicted statistics of output entities could be regarded as high-level information and be beneficial for other ontological operations, such as ontology filtering and ontology reasoning. The implementation of the SROM algorithm is non-intrusive to the mathematical model; therefore, this algorithm could be applicable to quantify uncertainty in ontologies with any mathematical relationships.
Áreas de investigación:
Año:
2017
Tipo de publicación:
Artículos en revistas científicas
Revista:
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volumen:
29
Número:
4
Páginas:
912-920
DOI:
10.1109/TKDE.2017.2651819
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