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Automatically Generating Government Linked Data from Tables
| Citations: | 2 - 0 self |
BibTeX
@MISC{Mulwad_automaticallygenerating,
author = {Varish Mulwad and Tim Finin and Anupam Joshi},
title = {Automatically Generating Government Linked Data from Tables},
year = {}
}
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Abstract
Most open government data is encoded and published in structured tables found in reports, on the Web, and in spreadsheets or databases. Current approaches to gener-ating Semantic Web representations from such data re-quires human input to create schemas and often results in graphs that do not follow best practices for linked data. Evidence for a table’s meaning can be found in its column headers, cell values, implicit relations between columns, caption and surrounding text but also requires general and domain-specific background knowledge. We describe techniques grounded in graphical models and probabilistic reasoning to infer meaning (seman-tics) associated with a table using background knowl-edge from the Linked Open Data cloud. We represent a table’s meaning by mapping columns to classes in an appropriate ontology, linking cell values to literal con-stants, implied measurements, or entities in the linked data cloud (existing or new) and discovering or and identifying relations between columns.
Keyphrases
government linked data cell value table meaning gener-ating semantic web representation current approach open government data appropriate ontology probabilistic reasoning graphical model structured table implicit relation domain-specific background knowledge open data cloud column header data cloud literal con-stants implied measurement data re-quires human input
