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CLC number: TP311

On-line Access: 2012-04-07

Received: 2011-08-13

Revision Accepted: 2012-02-13

Crosschecked: 2012-02-27

Cited: 1

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Journal of Zhejiang University SCIENCE C 2012 Vol.13 No.4 P.295-307


A multi-agent framework for mining semantic relations from Linked Data

Author(s):  Hua-jun Chen, Tong Yu, Qing-zhao Zheng, Pei-qin Gu, Yu Zhang

Affiliation(s):  School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   huajunsir@zju.edu.cn, ytcs@zju.edu.cn

Key Words:  Semantic Web, Linked open data, Semantic association discovery

Hua-jun Chen, Tong Yu, Qing-zhao Zheng, Pei-qin Gu, Yu Zhang. A multi-agent framework for mining semantic relations from Linked Data[J]. Journal of Zhejiang University Science C, 2012, 13(4): 295-307.

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T1 - A multi-agent framework for mining semantic relations from Linked Data
A1 - Hua-jun Chen
A1 - Tong Yu
A1 - Qing-zhao Zheng
A1 - Pei-qin Gu
A1 - Yu Zhang
J0 - Journal of Zhejiang University Science C
VL - 13
IS - 4
SP - 295
EP - 307
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1101010

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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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