Recovering semantics of tables on the web

Venetis, Petros; Halevy, Alon; Madhavan, Jayant; Paşca, Marius; Shen, Warren; Wu, Fei; Miao, Gengxin; Wu, Chung Wei

doi:10.14778/2002938.2002939

Cited by 258 publications

(325 citation statements)

References 25 publications

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“…In the last several years, an active and inventive group at Google, possibly inspired by Halevy, Norvig, and Pereira [37], collected and analyzed millions of tables harvested from the web [1,38,39]. Visual verification of their results has necessarily been restricted to much smaller samples.…”

Section: Physical Structure Extractionmentioning

confidence: 99%

Converting heterogeneous statistical tables on the web to searchable databases

Embley

Krishnamoorthy

Nagy

et al. 2016

IJDAR

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Much of the world's quantitative data resides in scattered web tables. For a meaningful role in Big Data analytics, the facts reported in these tables must be brought into a uniform framework. Based on a formalization of header-indexed tables, we proffer an algorithmic solution to end-to-end table processing for a large class of humanreadable tables. The proposed algorithms transform headerindexed tables to a category table format that maps easily to a variety of industry-standard data stores for query processing. The algorithms segment table regions based on the unique indexing of the data region by header paths, classify table cells, and factor header category structures of two-dimensional as well as the less common multidimensional tables. Experimental evaluations substantiate the algorithmic approach to processing heterogeneous tables. As demonstrable results, the algorithms generate queryable relational database tables and semantic-web triple stores. Application of our algorithms to 400 web tables randomly selected from diverse sources shows that the algorithmic solution automates end-to-end table processing. document analysis table segmentation table analysis table header factoring end-to-end table processing· table headers queries over table data ___________________________________________ Keywords

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Section: Physical Structure Extractionmentioning

confidence: 99%

Converting heterogeneous statistical tables on the web to searchable databases

Embley

Krishnamoorthy

Nagy

et al. 2016

IJDAR

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“…CSV and HTML tables can be turned into RDF with dedicated tools [14,21]. Larger frameworks, like Open Refine + DERI's RDF plugin [7,20], Opencube [13] On enriching the RDF graphs coming out of the statistical tables, [25] annotates Web tables using class labels and relationships automatically extracted from the Web to augment the semantics and improve access. Integrated HTML tables are used to extend search aggregated results [8,27] and to insert Web table data into word processing software [8].…”

Section: Related Workmentioning

confidence: 99%

“…For this reason, we update these statistics every time we run the conversion workflow. 25 . This allows us to keep track of what is left to map.…”

Section: Linked Dataset Descriptionmentioning

confidence: 99%

CEDAR: The Dutch historical censuses as Linked Open Data

Meroño‐Peñuela

Ashkpour

Guéret

et al. 2016

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Abstract. Here, we describe the CEDAR dataset, a five-star Linked Open Data representation of the Dutch historical censuses. These were conducted in the Netherlands once every 10 years from 1795 to 1971. We produce a linked dataset from a digitized sample of 2,288 tables. It contains more than 6.8 million statistical observations about the demography, labour and housing of Dutch society in the 18th, 19th and 20th centuries. The dataset is modeled using the RDF Data Cube, Open Annotation, and PROV vocabularies. These are used to represent the multidimensionality of the data, to express rules of data harmonization, and to keep track of the provenance of all data points and their transformations, respectively. We link observations within the dataset to well known standard classification systems in social history, such as the Historical International Standard Classification of Occupations (HISCO) and the Amsterdamse Code (AC). The three contributions of the dataset are (1) an easier access to integrated census data for historical researchers; (2) richer connections to related Linked Data resources; and (3) novel concept schemes of historical relevance, like classifications of historical religions and historical house types.

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“…Semantic annotation of services [21,22] and more recently of web tables [16,18,25] has also received attention. Most of this work learns types for services parameters or table columns, but is limited in learning relationships.…”

Section: Related Workmentioning

confidence: 99%

A Graph-Based Approach to Learn Semantic Descriptions of Data Sources

Taheriyan

Knoblock

Szekely

et al. 2013

Advanced Information Systems Engineering

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Abstract. Semantic models of data sources and services provide support to automate many tasks such as source discovery, data integration, and service composition, but writing these semantic descriptions by hand is a tedious and time-consuming task. Most of the related work focuses on automatic annotation with classes or properties of source attributes or input and output parameters. However, constructing a source model that includes the relationships between the attributes in addition to their semantic types remains a largely unsolved problem. In this paper, we present a graph-based approach to hypothesize a rich semantic description of a new target source from a set of known sources that have been modeled over the same domain ontology. We exploit the domain ontology and the known source models to build a graph that represents the space of plausible source descriptions. Then, we compute the top k candidates and suggest to the user a ranked list of the semantic models for the new source. The approach takes into account user corrections to learn more accurate semantic descriptions of future data sources. Our evaluation shows that our method produces models that are twice as accurate than the models produced using a state of the art system that does not learn from prior models.

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Recovering semantics of tables on the web

Cited by 258 publications

References 25 publications

Converting heterogeneous statistical tables on the web to searchable databases

Converting heterogeneous statistical tables on the web to searchable databases

CEDAR: The Dutch historical censuses as Linked Open Data

A Graph-Based Approach to Learn Semantic Descriptions of Data Sources

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