Transforming Web Tables to a Relational Database

Embley, David W.; Seth, Sharad; Nagy, George

doi:10.1109/icpr.2014.479

Cited by 14 publications

(32 citation statements)

References 21 publications

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“…Chen et al proposed an extraction system to convert spreadsheet data into relational tuples. Authors in [34] investigated the nature of HTML tables from the web. We have provided the comparison of related techniques in Table 2.…”

Section: Schema Extraction and Matchingmentioning

confidence: 99%

A Novel Approach to Data Extraction on Hyperlinked Webpages

2019

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The World Wide Web has an enormous amount of useful data presented as HTML tables. These tables are often linked to other web pages, providing further detailed information to certain attribute values. Extracting schema of such relational tables is a challenge due to the non-existence of a standard format and a lack of published algorithms. We downloaded 15,000 web pages using our in-house developed web-crawler, from various web sites. Tables from the HTML code were extracted and table rows were labeled with appropriate class labels. Conditional random fields (CRF) were used for the classification of table rows, and a nondeterministic finite automaton (NFA) algorithm was designed to identify simple, complex, hyperlinked, or non-linked tables. A simple schema for non-linked tables was extracted and for the linked-tables, relational schema in the form of primary and foreign-keys (PK and FK) were developed. Child tables were concatenated with the parent table’s attribute value (PK), serving as foreign keys (FKs). Resultantly, these tables could assist with performing better and stronger queries using the join operation. A manual checking of the linked web table results revealed a 99% precision and 68% recall values. Our 15,000-strong downloadable corpus and a novel algorithm will provide the basis for further research in this field.

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Section: Schema Extraction and Matchingmentioning

confidence: 99%

A Novel Approach to Data Extraction on Hyperlinked Webpages

2019

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“…Table 3 shows the distributions of the 198 non-trivial SAUS row and column header sizes. The data shows that multi-row column headers are more frequent (99) than multi-column row headers (64). The statistics on header sizes, prefixed rows and columns, number of row and column categories, and number of notes rows are based on analysis of the minimal indexing headers found by MIPS that do not depend on subjective interpretation of the table.…”

Section: Html Tables (Troy 200)mentioning

confidence: 99%

“…At the 2014 Document Analysis Systems workshop, we reported on our initial, automatic end-to-end conversion of web tables to relational databases [63]. We showed SQL queries on HTML tables imported into MS-Access at ICPR 2014 [64]. At the 2015 IST/SPIE Conference on Document Recognition and Retrieval, we clustered the headers of category hierarchies to reveal commonalities among tables [65].…”

Section: Our Earlier Workmentioning

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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“…The foundations for analyzing table headers that we developed over the years have been published in a succession of conference papers cited in our report at ICPR 2014 [25] and updated in our recent IJDAR article [1].…”

Section: Prior Workmentioning

confidence: 99%