学习发现从web表单到本体的复杂映射

Yuan An, Xiaohua Hu, I. Song
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引用次数: 10

摘要

为了实现语义Web,需要对Web上的各种结构(包括Web表单)进行注释,并将其映射到领域本体。我们提出了一种基于机器学习的自动方法来发现从Web表单到本体的复杂映射。复杂映射将表单上一组语义相关的元素与本体中一组语义相关的元素关联起来。现有的模式映射解决方案主要依赖于完整性约束来推断复杂的模式映射。然而,从表单中提取丰富的完整性约束是很困难的。我们将展示如何使用机器学习技术自动发现Web表单和本体之间的复杂映射。挑战在于如何捕获和学习编码在现有复杂映射中的复杂知识。我们开发了一个采用朴素贝叶斯方法的初始解决方案。我们评估了解决方案在不同领域的性能。我们的实验结果表明,对于超过80%的测试用例,该解决方案通常在数百个候选映射中返回预期映射的前1名结果。此外,期望的映射总是作为k<4的前k个结果返回。实验表明,该方法是有效的,有可能节省大量的人力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Learning to discover complex mappings from web forms to ontologies
In order to realize the Semantic Web, various structures on the Web including Web forms need to be annotated with and mapped to domain ontologies. We present a machine learning-based automatic approach for discovering complex mappings from Web forms to ontologies. A complex mapping associates a set of semantically related elements on a form to a set of semantically related elements in an ontology. Existing schema mapping solutions mainly rely on integrity constraints to infer complex schema mappings. However, it is difficult to extract rich integrity constraints from forms. We show how machine learning techniques can be used to automatically discover complex mappings between Web forms and ontologies. The challenge is how to capture and learn the complicated knowledge encoded in existing complex mappings. We develop an initial solution that takes a naive Bayesian approach. We evaluated the performance of the solution on various domains. Our experimental results show that the solution returns the expected mappings as the top-1 results usually among several hundreds candidate mappings for more than 80% of the test cases. Furthermore, the expected mappings are always returned as the top-k results with k<4. The experiments have demonstrated that the approach is effective and has the potential to save significant human efforts.
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