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arXiv 1307.6348cs.DB
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无序XML的学习模式

Learning Schemas for Unordered XML

Radu Ciucanu, Slawek Staworko

摘要:

我们认为无序的XML,兄弟姐妹之间的相对顺序被忽略,我们调查的问题,从用户给出的例子学习模式。本文主要讨论了[10]中提出的模式形式:析取多重模式(DMS)及其限制,无析取多重模式(MS)。学习算法将必须满足模式(即,正例)和必须不满足模式的一组XML文档(即,否定示例),并返回与示例一致的模式。我们研究了一个受Gold [18]启发的学习框架,其中学习算法应该是合理的,即,总是返回与用户给出的示例一致的模式,并完成,即,能够用足够丰富的示例集生成每个模式。此外,算法应该是有效的,即,多项式的输入大小。我们证明了DMS是可学习的,从正面的例子,但他们是不可学习的,当我们也允许负面的例子。此外,我们表明,MS是可学习的存在下,只有正面的例子,也在存在的正面和负面的例子。此外,对于可学习的情况下,所提出的学习算法返回的最小模式的例子一致。

英文摘要:

We consider unordered XML, where the relative order among siblings is ignored, and we investigate the problem of learning schemas from examples given by the user. We focus on the schema formalisms proposed in [10]: disjunctive multiplicity schemas (DMS) and its restriction, disjunction-free multiplicity schemas (MS). A learning algorithm takes as input a set of XML documents which must satisfy the schema (i.e., positive examples) and a set of XML documents which must not satisfy the schema (i.e., negative examples), and returns a schema consistent with the examples. We investigate a learning framework inspired by Gold [18], where a learning algorithm should be sound i.e., always return a schema consistent with the examples given by the user, and complete i.e., able to produce every schema with a sufficiently rich set of examples. Additionally, the algorithm should be efficient i.e., polynomial in the size of the input. We prove that the DMS are learnable from positive examples only, but they are not learnable when we also allow negative examples. Moreover, we show that the MS are learnable in the presence of positive examples only, and also in the presence of both positive and negative examples. Furthermore, for the learnable cases, the proposed learning algorithms return minimal schemas consistent with the examples.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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