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Front. Inform. Technol. Electron. Eng.  2012, Vol. 13 Issue (4): 295-307    DOI: 10.1631/jzus.C1101010
    
A multi-agent framework for mining semantic relations from Linked Data
Hua-jun Chen, Tong Yu, Qing-zhao Zheng, Pei-qin Gu, Yu Zhang
School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; College of Information, Zhejiang Sci-Tech University, Hangzhou 310018, China
A multi-agent framework for mining semantic relations from Linked Data
Hua-jun Chen, Tong Yu, Qing-zhao Zheng, Pei-qin Gu, Yu Zhang
School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; College of Information, Zhejiang Sci-Tech University, Hangzhou 310018, China
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摘要： Linked data is a decentralized space of interlinked Resource Description Framework (RDF) graphs that are published, accessed, and manipulated by a multitude of Web agents. Here, we present a multi-agent framework for mining hypothetical semantic relations from linked data, in which the discovery, management, and validation of relations can be carried out independently by different agents. These agents collaborate in relation mining by publishing and exchanging inter-dependent knowledge elements, e.g., hypotheses, evidence, and proofs, giving rise to an evidentiary network that connects and ranks diverse knowledge elements. Simulation results show that the framework is scalable in a multi-agent environment. Real-world applications show that the framework is suitable for interdisciplinary and collaborative relation discovery tasks in social domains.
关键词： Semantic Web;  Linked open data;  Semantic association discovery    
Abstract: Linked data is a decentralized space of interlinked Resource Description Framework (RDF) graphs that are published, accessed, and manipulated by a multitude of Web agents. Here, we present a multi-agent framework for mining hypothetical semantic relations from linked data, in which the discovery, management, and validation of relations can be carried out independently by different agents. These agents collaborate in relation mining by publishing and exchanging inter-dependent knowledge elements, e.g., hypotheses, evidence, and proofs, giving rise to an evidentiary network that connects and ranks diverse knowledge elements. Simulation results show that the framework is scalable in a multi-agent environment. Real-world applications show that the framework is suitable for interdisciplinary and collaborative relation discovery tasks in social domains.
Key words: Semantic Web    Linked open data    Semantic association discovery
收稿日期: 2011-08-13 出版日期: 2012-04-07
CLC:  TP311  
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Hua-jun Chen
Tong Yu
Qing-zhao Zheng
Pei-qin Gu
Yu Zhang

引用本文:

Hua-jun Chen, Tong Yu, Qing-zhao Zheng, Pei-qin Gu, Yu Zhang. A multi-agent framework for mining semantic relations from Linked Data. Front. Inform. Technol. Electron. Eng., 2012, 13(4): 295-307.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C1101010    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2012/V13/I4/295

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