InfoGather: entity augmentation and attribute discovery by holistic matching with web tables

By Mohamed Yakout, Kris Ganjam, Kaushik Chakrabarti, Surajit Chaudhuri. 2012

277 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

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

1. Ihab F. Ilyas
2. Eugene Wu
3. Xu Chu
4. Sanjay Krishnan
5. Steven Euijong Whang
6. Ziawasch Abedjan
7. Alon Y. Halevy
8. Jayant Madhavan

In Information Systems

1. Xu Chu
2. Ihab F. Ilyas
3. Steven Euijong Whang
4. Ziawasch Abedjan
5. Yuji Roh
6. Eugene Wu
7. Geon Heo
8. Alon Y. Halevy

In Management Science and Operations Research

1. Ihab F. Ilyas
2. Xu Chu
3. Eugene Wu
4. Steven Euijong Whang
5. Ziawasch Abedjan
6. Yuji Roh
7. Nan Tang
8. Geon Heo

Citations in the corpus

• A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, methodology): “To help the user decide which attributes to fill in, InfoGatherer identifies synonymous attributes in the Web tables.”
• Ten Years of WebTables (2018, background): “InfoGather [39] observed that precision and coverage of the filled-in values can be improved by exploiting indirect match tables that are a “hop away” from the original query table.”
• The Data Civilizer System (2017, methodology): “This is similar to the extend operator of Octopus [7], or the ABA operation of Infogather [23].”
• TEGRA: Table Extraction by Global Record Alignment (2015, background): “including table search [1, 2], table integration [9, 27], and knowledge discovery [26] among many others.”
• Navigating the Data Lake with DATAMARAN: Automatically Extracting Structure from Log Datasets (2017, unknown): “nnnnnnn datasets, and put them to use to aid analysis, search, or browsing [9, 10, 17, 32, 40, 48, 49, 51].”
• Goods: Organizing Google's Datasets (2016, background): “Researchers have proposed several systems [12, 26, 7] to extract HTML tables from the Web, to annotate their schema, and to implement a search service for this type of Webtables.”
• Aurum: A Data Discovery System (2018, methodology): “For example, systems such as Goods [1], Infogather [2] and Octopus [3] do not provide a flexible way to support different discovery queries than those they were designed to solve.”
• Data Lake Management: Challenges and Opportunities (2019, methodology): “Relevant data may be modeled as data that augments known entities with new attributes or properties, as done in Infogather [35].”
• Biperpedia: An Ontology for Search Applications (2014, background): “Several recent efforts have focused on harnessing such tables [1, 4, 17, 25, 26].”
• Methods for exploring and mining tables on Wikipedia (2013, background): “Yakout et. al. introduced the task of Attribute Discovery in [19], which involves finding columns of attributes from tables for a given query set of entities.”