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

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, ma...

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Bibliographic Details
Published in:Frontiers of information technology & electronic engineering 2012-04, Vol.13 (4), p.295-307
Main Authors: Chen, Hua-jun, Yu, Tong, Zheng, Qing-zhao, Gu, Pei-qin, Zhang, Yu
Format: Article
Language:English
Subjects:
Communications Engineering
Computer Hardware
Computer Science
Computer Systems Organization and Communication Networks
Data mining
Electrical Engineering
Electronics and Microelectronics
Exchanging
Graphs
Instrumentation
Multiagent systems
Networks
Semantics
Simulation
Tasks
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Summary: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.
ISSN:1869-1951
2095-9184
1869-196X
2095-9230
DOI:10.1631/jzus.C1101010