Collaborative Semantic Association Discovery from Linked Data

被引：0

作者：

Zheng, Qingzhao ^{[1
]}

Chen, Huajun ^{[1
]}

Yu, Tong ^{[1
]}

Pan, Gang ^{[1
]}

机构：

[1] Zhejiang Univ, Hangzhou, Zhejiang, Peoples R China

来源：

PROCEEDINGS OF THE 2009 IEEE INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION | 2008年

关键词：

Semantic Web; Linked Data; Semantic Association Discovery; Knowledge Discovery; Multi-Agent;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The efforts of publishing and interlinking structured data on the Semantic Web will result in a global network of databases, or the Linked Data, which provides huge potential for discovering hidden relationships. We present a multi-agent framework for Semantic Associations Discovery (SAD) from distributed linked data on the Semantic Web. Here, agents collaborate in SAD by publishing inter-dependent hypotheses and evidences, giving rise to an evidentiary network that connects and ranks diverse knowledge elements. We evaluate this framework through simulation, and the results show that the framework is suitable in cross-domain relationship discovery tasks.

引用

页码：394 / 399

页数：6

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