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Efficient Learning of Semi-structured Data from Queries

机译:从查询中高效学习半结构化数据

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This paper studies the polynomial-time learnability of the classes of ordered gapped tree patterns (OGT) and ordered gapped forests (OGF) under the into-matching semantics in the query learning model of Angluin. The class OGT is a model of semi-structured database query languages, and a generalization of both the class of ordered/unordered tree pattern languages and the class of non-erasing regular pattern languages. First, we present a polynomial time learning algorithm for μ-OGT, the subclass of OGT without repeated tree variables, using equivalence queries and membership queries. By extending this algorithm, we present polynomial time learning algorithms for the classes μ-OGF of forests without repeated variables and OGT of trees with repeated variables using equivalence queries and subset queries. We also give representation-independent hardness results which indicate that both of equivalence and membership queries are necessary to learn μ-OGT.
机译:本文研究了Angluin查询学习模型中不匹配语义下的有序树型树(OGT)和有序树型森林(OGF)的多项式时间可学习性。 OGT类是半结构化数据库查询语言的模型,是有序/无序树模式语言类和非擦除常规模式语言类的概括。首先,我们介绍了使用等价查询和隶属查询的μ-OGT(无重复树变量的OGT的子类)的多项式时间学习算法。通过扩展该算法,我们提出了使用等价查询和子集查询的无重复变量森林的μ-OGF类和具有重复变量的树木的OGT类的多项式时间学习算法。我们还给出了独立于表示的硬度结果,该结果表明,相等性和隶属度查询对于学习μ-OGT都是必要的。

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