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Guest editorial to the special issue on inductive logic programming, mining and learning in graphs and statistical relational learning

机译:客座社论涉及关于归纳逻辑编程,图形中的挖掘和学习以及统计关系学习的特殊问题

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摘要

In 2009, three international conferences/workshops on learning from relational, graph-based and probabilistic data were co-located: ILP-2009, the 19th International Conference on Inductive Logic Programming; MLG-2009, the 7th International Workshop on Mining and Learning with Graphs; and SRL-2009, the International Workshop on Statistical Relational Learning. These events were organized in Leuven, Belgium, on July 2-4, 2009. The ILP conference series has been the premier forum for work on logic-based approaches to learning for almost two decades and has recently reached out to other forms of relational learning and to probabilistic approaches. The MLG workshop series focuses on graph-based approaches to machine learning and data mining while the SRL workshop series focuses on statistical inference and learning with relational and first-order logical representations. The combination of probability theory with relational (or first-order logic) knowledge representation has been the subject of much recent research. While the three series clearly have their own identity, there is a significant overlap in the topics covered by each of them. In particular, the problem they study is essentially the same: learning from structured data involving multiple objects as well as the relationships that hold amongst them. The aim of the colocation was to increase the interaction between the three communities. The format of the joint event stimulated such interaction by providing joint invited speakers and tutorials, joint sessions and poster sessions, and ample time and space for discussions in smaller groups, in addition to the regular programs of the three events. For SRL and MLG, there were no formal proceedings though a post-conference proceedings has been published for ILP, in Volume 5989 of Springer's Lecture Notes in Artificial Intelligence series. In addition, the authors of eight papers, selected at the event, were invited to submit an extended version of their paper to the special issue, and of these, five papers were accepted after two rounds or reviewing.
机译:2009年,三个国际会议/讲习班共处一地,它们涉及从关系数据,基于图形的数据和概率数据中学习:ILP-2009,第19届国际归纳逻辑编程国际会议; MLG-2009,第七届国际图学与学习国际研讨会;和SRL-2009,国际统计关系学习讲习班。这些活动于2009年7月2-4日在比利时的鲁汶举行。ILP会议系列一直是基于逻辑的学习方法研究的主要论坛已有近二十年的历史,最近还涉及到其他形式的关系学习和概率方法。 MLG研讨会系列专注于基于图的机器学习和数据挖掘方法,而SRL研讨会系列专注于具有关系和一阶逻辑表示的统计推断和学习。概率论与关系(或一阶逻辑)知识表示的结合一直是许多近期研究的主题。虽然这三个系列显然具有自己的身份,但是每个系列所涵盖的主题之间存在重大重叠。特别地,他们研究的问题本质上是相同的:从涉及多个对象以及它们之间保持的关系的结构化数据中学习。托管的目的是增加三个社区之间的互动。联合活动的形式通过提供联合邀请的演讲者和教程,联合会议和海报会议以及除了三个活动的常规节目之外的小组讨论的充足时间和空间,激发了这种互动。对于SRL和MLG,尽管在Springer的“人工智能讲义”系列第5989卷中已为ILP发布了会后程序,但没有正式程序。此外,邀请了活动中选定的八篇论文的作者提交其论文的扩展版本,以供特刊使用。其中两轮或复审后,其中的五篇论文被接受。

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  • 来源
    《Machine Learning》 |2011年第2期|p.133-135|共3页
  • 作者单位

    Department of Computer Science, Katholieke Universiteit Leuven, Leuven, Belgium;

    Machine Learning and Computational Biology Research Group, Max Planck Institutes Tubingen,Spemannstr. 38, 72076 Tuebingen, Germany;

    Department of Computer Science, Katholieke Universiteit Leuven, Leuven, Belgium;

    Department of Computer Science and Engineering, University of Washington, Seattle, WA 98185, USA;

    Knowledge Discovery Department, Fraunhofer IAIS, Schloss Birlinghoven, 53754 Sankt Augustin,Germany;

    Computer Science Department, University of California, Santa Barbara, CA 93106, USA;

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  • 入库时间 2022-08-17 13:04:55

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