Recovering Semantics of Tables on the Web

By Petros Venetis, Alon Y. Halevy, Jayant Madhavan, Marius Pasca, Warren Shen, Fei Wu, Gengxin Miao, Chung Wu. Proceedings of the VLDB Endowment, 2011

383 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/c44cef69334cb62e6f6c7d0d245e1934f599815f

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Citations in the corpus

Knowledge vault: a web-scale approach to probabilistic knowledge fusion (2014, methodology): “Then we attempt to identify the relation that is expressed in each column of the table by looking at the entities in each column, and reasoning about which predicate each column could correspond to, by matching to Freebase, as in standard schema matching methods [39].”
Ten Years of WebTables (2018, methodology): “Diﬀerent methods built this space using knowledge bases [17], isA databases [32], or crowd sourcing [17].”
Methods for exploring and mining tables on Wikipedia (2013, methodology): “As one example, the semantic relatedness (SR) between Wikipedia pages estimated from the link graph [11] forms an extremely valuable feature for identifying relevant joins.”
YAGO2: A Spatially and Temporally Enhanced Knowledge Base from Wikipedia: Extended Abstract (2013, methodology): “These include KnowItAll and its successor TextRunner [4, 5], DBpedia [3], the Omnivore system [48], work on distilling Web tables and lists into facts [49, 50, 51], the ReadTheWeb project [52] the StatSnowball methods used for building EntityCube [53] and its follow-up project Probase [54], WikiNet ”
Semantic Web in data mining and knowledge discovery: A comprehensive survey (2016, background): “[40] Geography / H No No No YAGO, Freebase /”
TABBIE: Pretrained Representations of Tabular Data (2021, background): “…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 (Das Sarma et al., 2012), schema-matching…”
KATARA: A Data Cleaning System Powered by Knowledge Bases and Crowdsourcing (2015, methodology): “If most of the values in the films column also happen to be books , MaxLike will use books as the type, since there are fewer instances of books than films in Yago .”
Compositional Semantic Parsing on Semi-Structured Tables (2015, background): “There have been several studies on analyzing or inferring the table schemas (Cafarella et al., 2008; Venetis et al., 2011; Syed et al., 2010; Limaye et al., 2010) and answering search queries by joining tables on similar columns (Cafarella et al., 2008; Gonzalez et al., 2010; Pimplikar and…”
InfoGather: entity augmentation and attribute discovery by holistic matching with web tables (2012, methodology): “Furthermore, many web tables do not have column names [19]; this further exacerbates the problem.”
Seeping Semantics: Linking Datasets Using Word Embeddings for Data Discovery (2018, background): “For (ii), there are approaches for annotating Web tables using knowledge bases [38], [39], which is also known as table understanding.”
Data services leveraging Bing's data assets (2016, background): “Each relational table typically has one column that contains the names of the entities that correspond to the rows of the table [19, 20].”
Biperpedia: An Ontology for Search Applications (2014, methodology): “We used the subject-column annotator of WebTables and the class associated with it [24].”
Navigating the Data Lake with DATAMARAN: Automatically Extracting Structure from Log Datasets (2017, methodology): “Once we have structured these datasets, we can then infer relationships across datasets, and put them to use to aid analysis, search, or browsing [9, 10, 17, 32, 40, 48, 49, 51].”