大型结构化数据集的混合元数据分类

J. Data Intell. Pub Date : 2022-11-01 DOI:10.26421/jdi3.4-4
Sophie Pavia, Nick Piraino, Kazi Islam, A. Pyayt, M. Gubanov
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引用次数: 1

摘要

元数据定位与分类是大规模结构化数据集的一个重要问题。例如,Web表\cite{wt_corpus}有数以亿计的表,但是对于带有属性名的行(或列),往往缺少或不正确的标签。这种错误\cite{wtitles}严重地使所有数据管理任务复杂化,例如{\em查询处理、数据集成、索引}等。不同的来源或作者在表中以不同的方式定位元数据行/列,这使得其可靠识别变得困难。在这项工作中,我们描述了基于深度和机器学习的可扩展混合两层集成,结合长短期记忆(LSTM)和朴素贝叶斯分类器来准确识别表中包含行或列的元数据。我们对几个数据集进行了广泛的评估,包括一个超大规模的数据集,其中包含来自超过26,000个来源的超过1500万个表,以证明来自大量来源的可扩展性和抗多样性。与最近的方法相比,我们观察到这种双层集成的优越性,并报告使用常规LSTM的集成模型在规模上的95.73 \text{\%}精度令人印象深刻。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid Metadata Classification in Large-scale Structured Datasets
Metadata location and classification is an important problem for large-scale structured datasets. For example, Web tables \cite{wt_corpus} have hundreds of millions of tables, but often have missing or incorrect labels for rows (or columns) with attribute names. Such errors \cite{wtitles} significantly complicate all data management tasks such as {\em query processing, data integration, indexing}, etc. Different sources or authors position metadata rows/columns differently inside a table, which makes its reliable identification challenging.In this work we describe our scalable, hybrid two-layer Deep- and Machine-learning based ensemble, combining Long Short Term Memory (LSTM) and Naive Bayes Classifier to accurately identify Metadata-containing rows or columns in a table. We have performed an extensive evaluation on several datasets, including an ultra large-scale dataset containing more than 15 million tables coming from more than 26 thousands of sources to justify scalability and resistance to variety, stemming from a large number of sources. We observed superiority of this two-layer ensemble, compared to the recent previous approaches and report an impressive 95.73\text{\%} accuracy at scale with our ensemble model using regular LSTM.
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