An Exercise in Declarative Modeling for Relational Query Mining

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• 关键词：Knowledge representation ; Answer set programming ; Data mining ; Query mining ; Pattern mining
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9575
• 期：1
• 页码：166-182
• 全文大小：1,360 KB
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• 作者单位：Sergey Paramonov (16)
  Matthijs van Leeuwen (16)
  Marc Denecker (16)
  Luc De Raedt (16)
  
  16. KU Leuven, Celestijnenlaan 200A, 3001, Heverlee, Belgium
  
• 丛书名：Inductive Logic Programming
• ISBN：978-3-319-40566-7
• 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：9575

文摘

Motivated by the declarative modeling paradigm for data mining, we report on our experience in modeling and solving relational query and graph mining problems with the IDP system, a variation on the answer set programming paradigm. Using IDP or other ASP-languages for modeling appears to be natural given that they provide rich logical languages for modeling and solving many search problems and that relational query mining (and ILP) is also based on logic. Nevertheless, our results indicate that second order extensions to these languages are necessary for expressing the model as well as for efficient solving, especially for what concerns subsumption testing. We propose such second order extensions and evaluate their potential effectiveness with a number of experiments in subsumption as well as in query mining.