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Linking Semistructured Data on the Web
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@MISC{Yeganeh_linkingsemistructured,
author = {Soheil Hassas Yeganeh and Oktie Hassanzadeh and Renée J. Miller},
title = {Linking Semistructured Data on the Web},
year = {}
}
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Abstract
Many Web data sources and APIs make their data available in XML, JSON, or a domain-specific semi-structured format, with the goal of making the data easily accessible and usable by Web application developers. Although such data formats are more machine-processable than pure text documents, managing and analyzing such data in large scale is often nontrivial. This is mainly due to the lack of a well-defined (or understood) structure and clear semantics in such data formats, which could result in poor data quality. In the xCurator project, we add structure to such data with the goal of publishing it on the Web as Linked Data. We enhance the quality of such data by: extracting entities, their types, and their relationships to other entities; performing entity (and entity type) identification; merging duplicate entities (and entity types); linking related entities (internally and to external sources); and publishing the results on the Web as high-quality Linked Data. This is all in a light-weight easy-to-use and scalable framework that effectively incorporates user feedback in all phases. We describe the initial framework of our system and report the results of using our system for managing large volumes of (user-generated) data on the Web in several real world applications.
Keyphrases
semistructured data entity type data format many web data source linked data light-weight easy-to-use pure text document related entity high-quality linked data user feedback several real world application duplicate entity clear semantics xcurator project web application developer domain-specific semi-structured format large volume poor data quality extracting entity large scale scalable framework initial framework
