Automatic complex schema matching across Web query interfaces: A correlation mining approach
Automatic complex schema matching across Web query interfaces: A correlation mining approach
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DOI：
10.1145/1132863.1132872
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发表时间：
2006-03
期刊：
影响因子：
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通讯作者：
Bin He;K. Chang
中科院分区：
文献类型：
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作者：
Bin He;K. Chang
为了实现信息集成，模式匹配是发现跨异构源的属性的语义对应关系的关键步骤。虽然复杂匹配是常见的，但由于其复杂得多的搜索空间，大多数现有技术都专注于简单的1：1匹配。为了应对这一挑战，本文采取了一种概念上新颖的方法，将模式匹配视为相关挖掘，我们的任务是匹配Web查询接口，以集成互联网上的无数数据库。在这种“深层网络“上，查询接口通常在属性组之间形成复杂的匹配（例如，{author}对应于Books域中的{first name，last name}）。我们观察到跨查询接口的同现模式经常揭示这样复杂的语义关系：分组属性（例如，{名字，姓氏}）倾向于共同存在于查询界面中，因此正相关。相反，同义词属性是负相关的，因为它们很少同时出现。这种洞察力使我们能够通过相关挖掘方法发现复杂的匹配。特别是，我们开发的DCM框架，它包括数据预处理，双重挖掘的积极和消极的相关性，最后匹配建设。我们评估DCM框架手动提取的接口，结果显示出良好的准确性，发现复杂的匹配。此外，自动化的整个匹配过程中，我们采用自动技术的接口提取。在自动提取的接口上执行DCM框架，我们发现自动接口提取中不可避免的错误可能会显着影响匹配结果。为了使DCM框架对这种“嘈杂”的模式鲁棒性，我们将其与一种新的“合奏”的方法，它创建了一个合奏的DCM匹配器，通过随机化的模式数据到许多试验和聚合他们的排名结果，采取多数表决。作为一个原则性的基础上，我们提供的集成方法的鲁棒性的分析理由。从经验上讲，我们的实验表明，“集成”确实显着提高了匹配的准确性，自动提取，因此嘈杂的模式数据。通过采用DCM框架的集成方法，我们从而完成了一个自动匹配Web查询接口的过程。
To enable information integration, schema matching is a critical step for discovering semantic correspondences of attributes across heterogeneous sources. While complex matchings are common, because of their far more complex search space, most existing techniques focus on simple 1:1 matchings. To tackle this challenge, this article takes a conceptually novel approach by viewing schema matching as correlation mining, for our task of matching Web query interfaces to integrate the myriad databases on the Internet. On this “deep Web ” query interfaces generally form complex matchings between attribute groups (e.g., {author} corresponds to {first name, last name} in the Books domain). We observe that the co-occurrences patterns across query interfaces often reveal such complex semantic relationships: grouping attributes (e.g., {first name, last name}) tend to be co-present in query interfaces and thus positively correlated. In contrast, synonym attributes are negatively correlated because they rarely co-occur. This insight enables us to discover complex matchings by a correlation mining approach. In particular, we develop the DCM framework, which consists of data preprocessing, dual mining of positive and negative correlations, and finally matching construction. We evaluate the DCM framework on manually extracted interfaces and the results show good accuracy for discovering complex matchings. Further, to automate the entire matching process, we incorporate automatic techniques for interface extraction. Executing the DCM framework on automatically extracted interfaces, we find that the inevitable errors in automatic interface extraction may significantly affect the matching result. To make the DCM framework robust against such “noisy” schemas, we integrate it with a novel “ensemble” approach, which creates an ensemble of DCM matchers, by randomizing the schema data into many trials and aggregating their ranked results by taking majority voting. As a principled basis, we provide analytic justification of the robustness of the ensemble approach. Empirically, our experiments show that the “ensemblization” indeed significantly boosts the matching accuracy, over automatically extracted and thus noisy schema data. By employing the DCM framework with the ensemble approach, we thus complete an automatic process of matchings Web query interfaces.