The University of Southampton
University of Southampton Institutional Repository

Media fragment semantics: the linked data approach

Media fragment semantics: the linked data approach
Media fragment semantics: the linked data approach
In the last few years, the explosion of multimedia content on the Web has made multimedia resources the “first class citizen” of the Web. While these resources are easily stored and shared, it is becoming more difficult to find specific video/audio content, especially to identify, link, navigate, search and share the content inside multimedia resources. The concept of media fragment refers to the deep linking into multimedia resources, but making annotations to media fragments and linking them to other resources on the Web have yet to be adopted. The Linked Data principles offer guidelines for publishing Linked Data on the Web, so that data can be better connected to each other and explored by machines. Publishing media fragments and annotations as Linked Data will enable the media fragments to be transparently integrated into current Web content.

This thesis takes the Linked Data approach to realise the interlinking of media fragments to other resources on the Web and demonstrate how the Linked Data can help improve the indexing of media fragments. This thesis firstly identifies the gap between media fragments and Linked Data, and the major requirements that need to be fulfilled to bridge that gap based on the current situation of presenting and sharing multimedia data on the Web. Then, by extending the Linked Data principles, this thesis proposes Interlinking Media Fragment Principles as the basic rationale and best practice of applying Linked Data principles to media fragments. To further automate the media fragments publishing process, a core RDF model and a media fragment enriching framework are designed to link media fragments into the Linked Open Data Cloud via annotations and visualise media fragments on the Web pages. A couple of examples are implemented to demonstrate the use of interlinked media fragments, including the case to enrich YouTube videos with named entities and using media fragments for video classifications. The Media Fragment Indexing Framework is proposed to solve the fundamental problem of media fragments indexing for search engines and, as an example, Twitter is adopted as the source for media fragment annotations. The thesis concludes that applying Linked Data principles to media fragments will bring semantics to media fragments, which will improve the multimedia indexing on a fine-grained level and new research areas can be explored based on the interlinked media fragments.
Li, Yunjia
3a0d988e-b5e3-43c9-a268-dc14b5313547
Li, Yunjia
3a0d988e-b5e3-43c9-a268-dc14b5313547
Wald, Michael
90577cfd-35ae-4e4a-9422-5acffecd89d5
Wills, Gary
3a594558-6921-4e82-8098-38cd8d4e8aa0

Li, Yunjia (2015) Media fragment semantics: the linked data approach. University of Southampton, Electronics and Comupter Science, Doctoral Thesis, 192pp.

Record type: Thesis (Doctoral)

Abstract

In the last few years, the explosion of multimedia content on the Web has made multimedia resources the “first class citizen” of the Web. While these resources are easily stored and shared, it is becoming more difficult to find specific video/audio content, especially to identify, link, navigate, search and share the content inside multimedia resources. The concept of media fragment refers to the deep linking into multimedia resources, but making annotations to media fragments and linking them to other resources on the Web have yet to be adopted. The Linked Data principles offer guidelines for publishing Linked Data on the Web, so that data can be better connected to each other and explored by machines. Publishing media fragments and annotations as Linked Data will enable the media fragments to be transparently integrated into current Web content.

This thesis takes the Linked Data approach to realise the interlinking of media fragments to other resources on the Web and demonstrate how the Linked Data can help improve the indexing of media fragments. This thesis firstly identifies the gap between media fragments and Linked Data, and the major requirements that need to be fulfilled to bridge that gap based on the current situation of presenting and sharing multimedia data on the Web. Then, by extending the Linked Data principles, this thesis proposes Interlinking Media Fragment Principles as the basic rationale and best practice of applying Linked Data principles to media fragments. To further automate the media fragments publishing process, a core RDF model and a media fragment enriching framework are designed to link media fragments into the Linked Open Data Cloud via annotations and visualise media fragments on the Web pages. A couple of examples are implemented to demonstrate the use of interlinked media fragments, including the case to enrich YouTube videos with named entities and using media fragments for video classifications. The Media Fragment Indexing Framework is proposed to solve the fundamental problem of media fragments indexing for search engines and, as an example, Twitter is adopted as the source for media fragment annotations. The thesis concludes that applying Linked Data principles to media fragments will bring semantics to media fragments, which will improve the multimedia indexing on a fine-grained level and new research areas can be explored based on the interlinked media fragments.

PDF Thesis.pdf - Other
Download (6MB)

More information

Published date: June 2015
Organisations: University of Southampton, Web & Internet Science

Identifiers

Local EPrints ID: 377824
URI: https://eprints.soton.ac.uk/id/eprint/377824
PURE UUID: 33b6894a-f4be-4a80-bd70-b1ba55f300d8
ORCID for Gary Wills: ORCID iD orcid.org/0000-0001-5771-4088

Catalogue record

Date deposited: 13 Jul 2015 08:42
Last modified: 06 Jun 2018 13:03

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×