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

Hua-jun CHEN; Tong YU; Qing-zhao ZHENG; Pei-qin GU; Yu ZHANG

doi:10.1631/jzus.C1101010

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

用于更智能网络的语义技术专辑 | Updated：2022-05-19

- A multi-agent framework for mining semantic relations from Linked Data
- A multi-agent framework for mining semantic relations from Linked Data
- 信息与电子工程前沿（英文） 2012年13卷第4期页码：295-307
- Affiliations：
  
  1.School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
  2.College of Information, Zhejiang Sci-Tech University, Hangzhou 310018, China
- Author bio：
  
  [ "Hua-jun CHEN, E-mail: huajunsir@zju.edu.cn" ]
  Tong YU, E-mail: ytcs@zju.edu.cn
- Funds：
  
  Project supported by the National Natural Science Foundation of China (Nos. 61070156 and 61100183) and the Natural Science Foundation of Zhejiang Province, China (No. Y1110477)
- DOI：10.1631/jzus.C1101010
  中图分类号： TP311
- 收稿：2011-08-13，
  
  修回：2012-;2-27，
  
  纸质出版：2012-04
- Accepted：
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A multi-agent framework for mining semantic relations from Linked Data[J]. 信息与电子工程前沿（英文）, 2012,13(4):295-307.

Hua-jun CHEN, Tong YU, Qing-zhao ZHENG, et al. A multi-agent framework for mining semantic relations from Linked Data[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(4): 295-307.
A multi-agent framework for mining semantic relations from Linked Data[J]. 信息与电子工程前沿（英文）, 2012,13(4):295-307. DOI： 10.1631/jzus.C1101010.

Hua-jun CHEN, Tong YU, Qing-zhao ZHENG, et al. A multi-agent framework for mining semantic relations from Linked Data[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(4): 295-307. DOI： 10.1631/jzus.C1101010.

摘要

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.

关键词

Keywords

references

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