Evolving semantic web with social navigation

Beydoun, Ghassan; Kultchitsky, Roman; Manasseh, Grace

doi:10.1016/j.eswa.2005.11.035

Cited by 22 publications

(15 citation statements)

References 11 publications

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“…Learners' learning activities will be referred to as ratings because they are an expression of users' interests. Social science research has revealed that people build social relationships with each other and these relationships may help people in finding appropriate information or services more effectively [23]. Therefore, social network theory may be adapted to learning profiling.…”

Section: A Learner Profilingmentioning

confidence: 98%

Discovering Social Network to Improve Recommender System for Group Learning Support

Wan

Jamaliding

Okamoto

2009

2009 International Conference on Computational Intelligence and Software Engineering

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We refer to a new generation of web technologies such as social networking to address a recommender system that emphasize online collaborative learning. We propose an approach for improving recommender system through exploiting the learners note taking activity. We maintain that notes' features can be exploited by collaborative learning systems in order to enrich and extend the user profile and improve personalized learning. Thus our approach stresses collaborative note as a new and powerful kind of feedback and as a way to infer learner profiles. The experiment results show that our approach is effective.

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Section: A Learner Profilingmentioning

confidence: 98%

Discovering Social Network to Improve Recommender System for Group Learning Support

Wan

Jamaliding

Okamoto

2009

2009 International Conference on Computational Intelligence and Software Engineering

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“…An implicit semantic web developing at the heart of the system guides the interactions as in Beydoun, Kultchitsky, and Manasseh (2007) and Beydoun, (2009).…”

Section: Community-based P2p Information Sharing Masmentioning

confidence: 99%

Development of a peer-to-peer information sharing system using ontologies

Beydoun

Low

Tran

et al. 2011

Expert Systems with Applications

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a b s t r a c tMost existing agent-oriented methodologies ignore system extensibility, interoperability and reusability issues. Ontologies have been found to play a significant role in facilitating interoperability, reusability, MAS development activities (including MAS analysis and agent knowledge modeling) and MAS run-time operation (including agent communication and reasoning). However, most of the existing AOSE methodologies do not provide support for ontology-based MAS development. We present software engineering requirements for ontology-based development for MAS and examine an existing methodology, MOBMAS -a ''Methodology for Ontology-Based MASs'', which makes use of ontologies as a modeling tool. In this examination, we highlight how MOBMAS can be extended to utilizing ontology roles in facilitating interoperability, reusability and verification. This will be based on using MOBMAS to develop peer-topeer (P2P) community-based information sharing application which lead us to identify ontology related steps that can be added to enhance MOBMAS. MOBMAS is used by two experienced software developers who were not authors of the methodology, to guide the development of the P2P application.

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“…Moreover, such links are considered as the long links in the small world model. Beydoun et al present a "semantic annotation approach" to support search in a social network [1]. In a P2P network, we can also adopt a personalization scheme to suggest results (or change the ranking of the results) based on previous user feedback.…”

Section: Related Workmentioning

confidence: 99%

“…The default settings and the ranges of these parameters are listed in Table 1. [1,4] We vary the parameters to evaluate the performance of CYBER and PIF.…”

Section: Experiments Setupmentioning

confidence: 99%

Exploiting community feedback for information retrieval in DHT networks

Shou

Tan

2010

Peer-to-Peer Netw. Appl.

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In this paper, we propose CYBER, a CommunitY Based sEaRch engine, for information retrieval utilizing community feedback information in a DHT network. In CYBER, each user is associated with a set of user profiles that capture his/her interests. Likewise, a document is associated with a set of profilesone for each indexed term. A document profile is updated by users who query on the term and consider the document as a relevant answer. Thus, the profile acts as a consolidation of users feedback from the same community, and reflects their interests. In this way, as one user finds a document to be relevant, another user in the same community issuing a similar query will benefit from the feedback provided by the earlier user. Hence, the search quality in terms of both precision and recall is improved. Moreover, we further improve the effectiveness of CYBER by introducing an index tuning technique. By choosing the indexing terms more carefully, community-based relevance feedback is utilized in both building/refining indices and re-evaluating queries. We first propose a naive scheme, CYBER+, which involves an index tuning technique based on past queries only, and then re-evaluates queries in a separate step. We then propose a more complex scheme, CYBER++, which refines its index based on both past queries and relevance feedback. As the index is built with more selective and accurate terms, the search performance is further improved. We conduct a comprehensive experimental study and the results show the effectiveness of our schemes.

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Evolving semantic web with social navigation

Cited by 22 publications

References 11 publications

Discovering Social Network to Improve Recommender System for Group Learning Support

Discovering Social Network to Improve Recommender System for Group Learning Support

Development of a peer-to-peer information sharing system using ontologies

Exploiting community feedback for information retrieval in DHT networks

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