Methods for exploring and mining tables on Wikipedia
By Chandra Bhagavatula, Thanapon Noraset, Doug Downey. 2013
110 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Molecular Biology, Information Extraction
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Caiming Xiong
- Richard Socher
- Mohit Bansal
- William Yang Wang
- Jianfeng Gao
- Wenhu Chen
- Luke S. Zettlemoyer
- Yejin Choi
In Molecular Biology
- Petar Ristoski
- William Yang Wang
- Jianshu Chen
- Heiko Paulheim
- Richard Socher
- Caiming Xiong
- Victor Zhong
In Information Extraction
Citations in the corpus
- Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning (2017, methodology): “In the paraphrase phase, we use tables extracted from Wikipedia by Bhagavatula et al. (2013) and remove small tables according to the following criteria: • the number of cells in each row is not the same • the content in a cell exceed 50 characters • a header cell is empty • the table has less than…”
- TabFact: A Large-scale Dataset for Table-based Fact Verification (2019, methodology): “(Pasupat & Liang, 2015), Sequential Q&A (Iyyer et al., 2017), and WikiSQL (Zhong et al., 2017), for which approaches have been extended to handle large-scale resources like Wikipedia (Bhagavatula et al., 2013).”
- HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data (2020, background): “As Wikitables (Bhaga-vatula et al., 2013) are curated from high-standard professionals to organize a set of information regarding a given theme, its information is mostly absent in the text.”
- Semantic Web in data mining and knowledge discovery: A comprehensive survey (2016, background): “[49], where no LOD knowledge base is used, but only a metadata for the entities types and relations between them is added.”