WebTables: exploring the power of tables on the web

By Michael J. Cafarella, Alon Y. Halevy, Daisy Zhe Wang, Eugene Wu, Yang Zhang. 2008

765 citations recorded by the source corpus.

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

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Citations in the corpus

A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, background): “One of the most successful systems is WebTables [24], [25], which automatically”
Recovering Semantics of Tables on the Web (2011, methodology): “To recover semantics of tables, we leverage a database of class labels and relationships automatically extracted from the Web.”
Compositional Semantic Parsing on Semi-Structured Tables (2015, background): “, 2010) and answering search queries by joining tables on similar columns (Cafarella et al., 2008; Gonzalez et al., 2010; Pimplikar and Sarawagi, 2012).”
Data services leveraging Bing's data assets (2016, methodology): “These are challenging problems as they cannot be accomplished via simple rules [5].”
TABBIE: Pretrained Representations of Tabular Data (2021, background): “…of structured relational data in the form of tables on the Internet has attracted considerable attention from researchers over the past two decades (Cafarella et al., 2008; Limaye et al., 2010; Venetis et al., 2011; Suchanek et al., 2007; Embley et al., 2006), with applications including retrieval…”
ReNoun: Fact Extraction for Nominal Attributes (2014, background): “Other classes of attributes that we do not consider in this work are (1) numeric (e.g., population, GDP ) that are better extracted from Web tables (Cafarella et al., 2008), and (2) vague (e.g., culture, economy ) whose value is a narrative that would not ﬁt the current mode of query answering on…”
Knowledge vault: a web-scale approach to probabilistic knowledge fusion (2014, background): “The confidence scores from each extractor (and/or the fused system) are not necessarily on the same scale, and cannot necessarily be interpreted as probabilities.”
Data Curation at Scale: The Data Tamer System (2013, background): “This is the world of systems like Web tables [8].”
Ten Years of WebTables (2018, methodology): “In this section we provide a short overview of the original WebTables paper [7] and its core contributions.”
Data Integration for the Relational Web (2009, background): “( Cafarella et al. attempted this approach when ranking extracted HTML tables [4], which do often carry metadata in the form of per-column labels.)”
Annotating and searching web tables using entities, types and relationships (2010, methodology): “Another common task is to extract instances of relations between named entities from free-form text, e.g., “company A acquired company B for amount M” (see [19] for a survey).”
Ground: A Data Context Service (2017, background): “Schema extraction is one important example, in a spectrum from automated techniques [6, 1] to human-guided metadata wrangling [18, 20].”
Knowledge Graphs (2020, neutral): “[81, 108].”
Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions (2015, background): “types of data and these types of data also need to be linked with the knowledge base, such as web tables [140], [141], web lists [142], [143], and tweets [144], [145].”
InfoGather: entity augmentation and attribute discovery by holistic matching with web tables (2012, methodology): “The challenge is to be robust to spuriously matched tables: we address it by developing a holistic matching framework based on topic sensitive pagerank and an augmentation framework that aggregates predictions from multiple matched tables.”