Communities and Emerging Semantics in Semantic Link Network: Discovery and Learning

Abstract

Abstract-TheWorld Wide Web provides plentiful contents for Web-based learning, but its hyperlink-based architecture connects Web resources for browsing freely rather than for effective learning. To support effective learning, an e-learning system should be able to discover and make use of the semantic communities and the emerging semantic relations in dynamic complex network of learning resources. Previous graph-based community discovery approaches are limited in ability to discover semantic communities. This paper firstly suggests the Semantic Link Network SLN, a loosely coupled semantic data model that can semantically link resources and derive out implicit semantic links according to a set of relational reasoning rules. By studying the intrinsic relationship between semantic communities and the semantic space of SLN, approaches to discovering reasoning-constraint, rule-constaint and classification-constraint semantic communities are proposed. Further, the approaches, principles and strategies for discovering emerging semantics in dynamic SLN are studied. An e-learning environment incorporating the proposed approaches, principles and strategies to support effective discovery and learning is suggested.

Enhancing Grid Service Discovery with a Semantic Wiki and the Concept Matching Approach

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Cyber Physical Socio Ecology

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Semantic tags generation and retrieval for online advertising

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