Evaluation and Improvement of Semantically-Enhanced Tagging System
type: CreativeWork
description: The Social Web or ‘Web 2.0’ is focused on the interaction and collaboration between web sites users. It is credited for the existence of tagging systems, amongst other things such as blogs and Wikis. Tagging systems like YouTube and Flickr offer their users the simplicity and freedom in creating and sharing their own contents and thus folksonomy is a very active research area where many improvements are presented to overcome existing disadvantages such as the lack of semantic meaning, ambiguity, and inconsistency. TE is a tagging system proposing solutions to the problems of multilingualism, lack of semantic meaning and shorthand writing (which is very common in the social web) through the aid of semantic and social resources. The current research is presenting an addition to the TE system in the form of an embedded stemming component to provide a solution to the different lexical form problems. Prior to this, the TE system had to be explored thoroughly and then its efficiency had to be determined in order to decide on the practicality of embedding any additional components as enhancements to the performance. Deciding on this involved analysing the algorithm efficiency using an analytical approach to determine its time and space complexity. The TE had a time growth rate of O (N²) which is polynomial, thus the algorithm is considered efficient. Nonetheless, recommended modifications like patch SQL execution can improve this. Regarding space complexity, the number of tags per photo represents the problem size which, if it grows, will increase linearly the required memory space. Based on the findings above, the TE system is re-implemented on Flickr instead of YouTube, because of a recent YouTube restriction, which is of greater benefit in multi languages tagging system since the language barrier is meaningless in this case. The re-implementation is achieved using ‘flickrj’ (Java Interface for Flickr APIs). Next, the stemming component is added to perform tags normalisa…
abstract: The Social Web or ‘Web 2.0’ is focused on the interaction and collaboration between web sites users. It is credited for the existence of tagging systems, amongst other things such as blogs and Wikis. Tagging systems like YouTube and Flickr offer their users the simplicity and freedom in creating and sharing their own contents and thus folksonomy is a very active research area where many improvements are presented to overcome existing disadvantages such as the lack of semantic meaning, ambiguity, and inconsistency. TE is a tagging system proposing solutions to the problems of multilingualism, lack of semantic meaning and shorthand writing (which is very common in the social web) through the aid of semantic and social resources. The current research is presenting an addition to the TE system in the form of an embedded stemming component to provide a solution to the different lexical form problems. Prior to this, the TE system had to be explored thoroughly and then its efficiency had to be determined in order to decide on the practicality of embedding any additional components as enhancements to the performance. Deciding on this involved analysing the algorithm efficiency using an analytical approach to determine its time and space complexity. The TE had a time growth rate of O (N²) which is polynomial, thus the algorithm is considered efficient. Nonetheless, recommended modifications like patch SQL execution can improve this. Regarding space complexity, the number of tags per photo represents the problem size which, if it grows, will increase linearly the required memory space. Based on the findings above, the TE system is re-implemented on Flickr instead of YouTube, because of a recent YouTube restriction, which is of greater benefit in multi languages tagging system since the language barrier is meaningless in this case. The re-implementation is achieved using ‘flickrj’ (Java Interface for Flickr APIs). Next, the stemming component is added to perform tags normalisa…
author: Alsharif, Majdah Hussain
keywords: tags, web 2.0, semantic web, web 3.0, metadata
license: https://dora.dmu.ac.uk/bitstreams/ab8aff79-5f9f-4161-b56d-c30e9b75697a/download
date published: 2013-12
date modified: 2026-07-24T06:18:37Z
provider: De Montfort University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: De Montfort University