在维基百科上探索和挖掘表格的方法

Chandra Bhagavatula, Thanapon Noraset, Doug Downey
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引用次数: 92

摘要

从Web中自动提取的知识库为数据挖掘和探索提供了新的机会。给定一个庞大的、异构的抽取关系集,需要新的工具来搜索知识和发现感兴趣的关系。我们介绍WikiTables,这是一个Web应用程序,使用户能够交互式地探索从Wikipedia中提取的表格知识。在实验中,我们发现WikiTables在自动连接不同的表以发现表列之间的“有趣”关系的新任务上大大优于基线。我们发现,利用维基百科链接结构的“语义相关性”度量是这种改进的主要原因。此外,在表的关键字搜索任务上,我们表明wikittables的性能与Google Fusion tables相当,尽管使用的表少了一个数量级。我们的工作还包括发布大量公共资源,包括超过1500万个提取的表格数据元组、手动注释的评估集和公共api。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Methods for exploring and mining tables on Wikipedia
Knowledge bases extracted automatically from the Web present new opportunities for data mining and exploration. Given a large, heterogeneous set of extracted relations, new tools are needed for searching the knowledge and uncovering relationships of interest. We present WikiTables, a Web application that enables users to interactively explore tabular knowledge extracted from Wikipedia. In experiments, we show that WikiTables substantially outperforms baselines on the novel task of automatically joining together disparate tables to uncover "interesting" relationships between table columns. We find that a "Semantic Relatedness" measure that leverages the Wikipedia link structure accounts for a majority of this improvement. Further, on the task of keyword search for tables, we show that WikiTables performs comparably to Google Fusion Tables despite using an order of magnitude fewer tables. Our work also includes the release of a number of public resources, including over 15 million tuples of extracted tabular data, manually annotated evaluation sets, and public APIs.
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