学习使用Alchemy笔记之二

    总算把Alchemy的入门指南全部过了一遍，关于open-world、closed-world以及evidence predicate，它是这样说的：

• If the closed-world assumption is made for a predicate, its ground atoms that are not defined in a .db file are
  false, while if the open world assumption is made, its undefined ground atoms are unknown.
• An evidence predicate is defined as a predicate of which the .db evidence file contains at least one grounding; all evidence predicates are closed-world by default.
• The user may specify that some evidence predicates are open-world by listing them with the -o option. Also, the user may specify that some
  non-evidence predicates are closed-world by listing them with the -c option.

    还提到learnstruct能用来学习马尔科夫逻辑网的结构，但怎么用呢一直没说。

    后来幸运地在http://alchemy.cs.washington.edu/papers找到三篇论文介绍马尔科夫逻辑网的结构学习方面的内容，而且在http://alchemy.cs.washington.edu/data中有相应的例子，哈哈有一阵子好啃了：

• Kok, Stanley and Domingos, Pedro (2005). Learning the Structure of Markov Logic Networks. In Proceedings of the Twenty-Second International Conference on Machine Learning (pp. 441-448), 2005. Bonn, Germany: ACM Press.
• Kok, Stanley and Domingos, Pedro (2007). Statistical Predicate Invention. In Proceedings of the Twenty-Fourth International Conference on Machine Learning (pp. 433-440), 2007. Corvallis, Oregon: ACM Press.
• Mihalkova, Lily and Mooney, Raymond J. (2007). Bottom-Up Learning of Markov Logic Network Structure. In Proceedings of the 24th International Conference on Machine Learning (ICML-07). pp. 625-632.