Semantic Selection of Internet Sources through SWRL Enabled OWL Ontologies

Almarri, H. 2017. Semantic Selection of Internet Sources through SWRL Enabled OWL Ontologies. PhD thesis University of Westminster Computer Science

TitleSemantic Selection of Internet Sources through SWRL Enabled OWL Ontologies
TypePhD thesis
AuthorsAlmarri, H.

This research examines the problem of Information Overload (IO) and give an overview of various attempts to resolve it. Furthermore, argue that instead of fighting IO, it is advisable to start learning how to live with it. It is unlikely that in modern information age, where users are producer and consumer of information, the amount of data and information generated would decrease. Furthermore, when managing IO, users are confined to the algorithms and policies of commercial Search Engines and Recommender Systems (RSs), which create results that also add to IO. this research calls to initiate a change in thinking: this by giving greater power to users when addressing the relevance and accuracy of internet searches, which helps in IO. However powerful search engines are, they do not process enough semantics in the moment when search queries are formulated. This research proposes a semantic selection of internet sources, through SWRL enabled OWL ontologies. the research focuses on SWT and its Stack because they (a)secure the semantic interpretation of the environments where internet searches take place and (b) guarantee reasoning that results in the selection of suitable internet sources in a particular moment of internet searches. Therefore, it is important to model the behaviour of users through OWL concepts and reason upon them in order to address IO when searching the internet. Thus, user behaviour is itemized through user preferences, perceptions and expectations from internet
searches. The proposed approach in this research is a Software Engineering (SE) solution which provides computations based on the semantics of the environment stored in the ontological model.


Related outputs

Semantic Recommendation of Information Sources for Lifelong Learning
Almarri, H., Rahman, T., Juric, R. and Parapadakis, D. 2013. Semantic Recommendation of Information Sources for Lifelong Learning. Journal of Integrated Design and Process Science. 17 (1), pp. 55-78 5. doi:10.3233/jid-2013-0005

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