Communities and Emerging Semantics in Semantic Link Network: Discovery and Learning
Communities and Emerging Semantics in Semantic Link Network: Discovery and Learning
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DOI：
10.1109/tkde.2008.141
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发表时间：
2009-06
影响因子：
8.9
通讯作者：
H. Zhuge
中科院分区：
文献类型：
--
作者：
H. Zhuge
万维网为基于网络的学习提供了丰富的内容，但其基于超链接的结构连接网络资源是为了自由浏览而不是为了有效的学习。为了支持有效的学习，一个电子学习系统应该能够发现和利用语义社区和新兴的语义关系，在一个动态的复杂网络的学习资源。以前的基于图的社区发现方法在发现语义社区的能力上是有限的。本文首先提出了语义链接网络（SLN），一个松散耦合的语义数据模型，可以语义链接的资源，并根据一组关系推理规则派生出隐含的语义链接。通过研究语义社区与SLN语义空间的内在联系，提出了推理约束、规则约束和分类约束语义社区的发现方法。进一步研究了在动态SLN中发现新兴语义的方法、原则和策略。首次揭示了语义链接网络运动的基本规律。一个电子学习环境，结合建议的方法，原则和策略，以支持有效的发现和学习。
The World 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 a dynamic complex network of learning resources. Previous graph-based community discovery approaches are limited in ability to discover semantic communities. This paper first 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-constraint, and classification-constraint semantic communities are proposed. Further, the approaches, principles, and strategies for discovering emerging semantics in dynamic SLNs are studied. The basic laws of the semantic link network motion are revealed for the first time. An e-learning environment incorporating the proposed approaches, principles, and strategies to support effective discovery and learning is suggested.