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  1. Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA '13)
  2. Methods for exploring and mining tables on Wikipedia
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Interactive visual analytics for high dimensional data
Building blocks for exploratory data analysis tools
Methods for exploring and mining tables on Wikipedia
One click mining: interactive local pattern discovery through implicit preference and performance learning
Lytic: synthesizing high-dimensional algorithmic analysis with domain-agnostic, faceted visual analytics
A process-centric data mining and visual analytic tool for exploring complex social networks
Zips: mining compressing sequential patterns in streams
Augmenting MATLAB with semantic objects for an interactive visual environment
Online spatial data analysis and visualization system
Randomly sampling maximal itemsets
Towards anytime active learning: interrupting experts to reduce annotation costs
Storygraph: extracting patterns from spatio-temporal data

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Methods for exploring and mining tables on Wikipedia

Content Provider ACM Digital Library
Author Noraset, Thanapon Downey, Doug Bhagavatula, Chandra Sekhar
Abstract 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.
Starting Page 18
Ending Page 26
Page Count 9
File Format PDF
ISBN 9781450323291
DOI 10.1145/2501511.2501516
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2013-08-11
Publisher Place New York
Access Restriction Subscribed
Content Type Text
Resource Type Article
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