Quality Prediction in Collaborative Platforms: A Generic Approach by Heterogeneous Graphs

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• 关键词：Link ; analysis ; Heterogeneous graphs ; Quality
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9828
• 期：1
• 页码：19-26
• 全文大小：320 KB
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• 作者单位：Baptiste de La Robertie (15)
  Yoann Pitarch (15)
  Olivier Teste (15)
  
  15. Institut de Recherche en Informatique de Toulouse, 118 Route de Narbonne, 31071, Toulouse, France
  
• 丛书名：Database and Expert Systems Applications
• ISBN：978-3-319-44406-2
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9828

文摘

As everyone can enrich or rather impoverish crowd-sourcing contents, it is a crucial need to continuously improve automatic quality contents assessment tools. Structural-based analysis methods developed for such quality prediction purposes generally handle a limited or manually fixed number of families of nodes and relations. This lack of genericity prevents existing algorithms for being adaptable to platforms evolutions. In this work, we propose a generic and adaptable algorithm, called HSQ, generalising various state-of-the-art models and allowing the consideration of graphs defined by an arbitrary number of nodes semantics. Evaluations performed over the two representative crowd-sourcing platforms Wikipedia and Stack Exchange state that the consideration of additional nodes semantics and relations improve the performances of state-of-the-art approaches.