Journal article
The uncertainty mapping of ontologies based on three-dimensional combination weight vector space model
Information discovery and delivery, Vol.46(2), pp.110-119
05/21/2018
DOI: 10.1108/IDD-02-2017-0008
Abstract
This paper aims to apply vector space model (VSM)-PCR model to compute the similarity of Fault zone ontology semantics, which verified the feasibility and effectiveness of the application of VSM-PCR method in uncertainty mapping of ontologies.
Design/methodology/approach
The authors first define the concept of uncertainty ontology and then propose the method of ontology mapping. The proposed method fully considers the properties of ontology in measuring the similarity of concept. It expands the single VSM of concept meaning or instance set to the “meaning, properties, instance” three-dimensional VSM and uses membership degree or correlation to express the level of uncertainty.
Findings
It provides a relatively better accuracy which verified the feasibility and effectiveness of VSM-PCR method in treating the uncertainty mapping of ontology.
Research limitations/implications
The future work will focus on exploring the similarity measure and combinational methods in every dimension.
Originality/value
This paper presents an uncertain mapping method of ontology concept based on three-dimensional combination weighted VSM, namely, VSM-PCR. It expands the single VSM of concept meaning or instance set to the “meaning, properties, instance” three-dimensional VSM. The model uses membership degree or correlation which is used to express the degree of uncertainty; as a result, a three-dimensional VSM is obtained. The authors finally provide an example to verify the feasibility and effectiveness of VSM-PCR method in treating the uncertainty mapping of ontology.
Details
- Title: Subtitle
- The uncertainty mapping of ontologies based on three-dimensional combination weight vector space model
- Creators
- Dongmei Han - Department of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, ChinaWen Wang - Fudan University, Shanghai, ChinaSuyuan Luo - Department of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, ChinaWeiguo Fan - Department of Accounting and Information Systems, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USASongxin Wang - Department of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, China
- Resource Type
- Journal article
- Publication Details
- Information discovery and delivery, Vol.46(2), pp.110-119
- DOI
- 10.1108/IDD-02-2017-0008
- ISSN
- 2398-6247
- eISSN
- 2398-6255
- Language
- English
- Date published
- 05/21/2018
- Academic Unit
- Business Analytics
- Record Identifier
- 9984083892102771
Metrics
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