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Apply the linked data principles to a decision support system for accessible travelling

Apply the linked data principles to a decision support system for accessible travelling
Apply the linked data principles to a decision support system for accessible travelling
The development of Information Communication Technologies (ICTs) empowers digital inclusion, which can bring benefits to all people. However, according to the literature review of current research related to accessible travelling, most projects are focused on improving assistive technologies, devices, services, and user interfaces regardless of whether they deliver useful or optimised content to satisfy users’ special needs. Limitations and challenges are exposed from these projects which are aiming to address the accessible travelling problems faced by people with disabilities, namely the lack of accessibility metadata, data isolation, urgent needs of methods for data integration, and better algorithms for decision making. Therefore, the conceptual model of Linked Data-driven decision support system (DSS) for accessible travelling is proposed in this research to address these problems. Using a Linked Data driven approach, this research explores the areas of open accessibility data, accessibility data integration and interlinking. Moreover, there are three the ontologies proposed to address the needs of standard vocabularies to publish open accessibility data. Furthermore, the research also discusses and demonstrates strategies to apply the decision support methods to Linked Data. As a result, this research would not only benefit people with disabilities or special needs but also contribute to the research of a novel model to address accessibility information barriers and accessible travel planning issues by applying the Linked Data principles to DSSs to provide decision support.
University of Southampton
Ding, Chaohai
d07f64ec-a5c8-4167-9d34-9a3913e4dd1a
Ding, Chaohai
d07f64ec-a5c8-4167-9d34-9a3913e4dd1a
Wald, Michael
90577cfd-35ae-4e4a-9422-5acffecd89d5

Ding, Chaohai (2017) Apply the linked data principles to a decision support system for accessible travelling. University of Southampton, Doctoral Thesis, 202pp.

Record type: Thesis (Doctoral)

Abstract

The development of Information Communication Technologies (ICTs) empowers digital inclusion, which can bring benefits to all people. However, according to the literature review of current research related to accessible travelling, most projects are focused on improving assistive technologies, devices, services, and user interfaces regardless of whether they deliver useful or optimised content to satisfy users’ special needs. Limitations and challenges are exposed from these projects which are aiming to address the accessible travelling problems faced by people with disabilities, namely the lack of accessibility metadata, data isolation, urgent needs of methods for data integration, and better algorithms for decision making. Therefore, the conceptual model of Linked Data-driven decision support system (DSS) for accessible travelling is proposed in this research to address these problems. Using a Linked Data driven approach, this research explores the areas of open accessibility data, accessibility data integration and interlinking. Moreover, there are three the ontologies proposed to address the needs of standard vocabularies to publish open accessibility data. Furthermore, the research also discusses and demonstrates strategies to apply the decision support methods to Linked Data. As a result, this research would not only benefit people with disabilities or special needs but also contribute to the research of a novel model to address accessibility information barriers and accessible travel planning issues by applying the Linked Data principles to DSSs to provide decision support.

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Published date: September 2017

Identifiers

Local EPrints ID: 415899
URI: http://eprints.soton.ac.uk/id/eprint/415899
PURE UUID: 35374e82-2131-4322-b8a0-efcb7ac644d2

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Date deposited: 28 Nov 2017 17:30
Last modified: 15 Mar 2024 17:02

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Contributors

Author: Chaohai Ding
Thesis advisor: Michael Wald

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