Semantic Selection of Internet Sources through SWRL Enabled OWL Ontologies

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

Improving accuracy of recommender systems through triadic closure

The exponential growth of social media services led to the information overload problem which information filtering and recommender systems deal by exploiting various techniques. One popular technique for making recommendations is based on trust statements between users in a social network. Yet explicit trust statements are usually very sparse leading to the need for expanding the trust networks by inferring new trust relationships. Existing methods exploit the propagation property of trust to expand the existing trust networks; however, their performance is strongly affected by the density of the trust network. Nevertheless, the utilisation of existing trust networks can model the users’ relationships, enabling the inference of new connections. The current study advances the existing methods and techniques on developing a trust-based recommender system proposing a novel method to infer trust relationships and to achieve a fully-expanded trust network. In other words, the current study proposes a novel, effective and efficient approach to deal with the information overload by expanding existing trust networks so as to increase accuracy in recommendation systems. More specifically, this study proposes a novel method to infer trust relationships, called TriadicClosure. The method is based on the homophily phenomenon of social networks and, more specifically, on the triadic closure mechanism, which is a fundamental mechanism of link formation in social networks via which communities emerge naturally, especially when the network is very sparse. Additionally, a method called JaccardCoefficient is proposed to calculate the trust weight of the inferred relationships based on the Jaccard Cofficient similarity measure. Both the proposed methods exploit structural information of the trust graph to infer and calculate the trust value. Experimental results on real-world datasets demonstrate that the TriadicClosure method outperforms the existing state-of-the-art methods by substantially improving prediction accuracy and coverage of recommendations. Moreover, the method improves the performance of the examined state-of-the-art methods in terms of accuracy and coverage when combined with them. On the other hand, the JaccardCoefficient method for calculating the weight of the inferred trust relationships did not produce stable results, with the majority showing negative impact on the performance, for both accuracy and coverage