Presented at: 9th Extended Semantic Web Conference (ESWC2012)
The Linked Data cloud contains large amounts of RDF generated from databases. Much of this RDF, generated using tools such as D2R, is expressed in terms of vocabularies automatically derived from the schema of the original database. The generated RDF would be significantly more useful if it was expressed in terms of commonly used vocabularies. Defining the mapping from structured sources such as databases or spreadsheets to ontologies is labor intensive using today’s tools. For example, to define such mappings in R2R, users must write a mapping rule for each column, and each mapping is expressed in terms of graph patterns, which are hard to write. In this work, we present a semi-automatic approach for building mappings from structured sources to ontologies. Our system, Karma, automatically derives these mappings, and provides an easy to use interface that enables users to control the automated process to guide the system to produce the desired mappings. In our evaluation, users need to interact with the system less than once per column (on average) in order to construct the desired mapping rules. The system then users these mapping rules to generate semantically rich RDF for the data sources.
Keywords: RDF generation, source mapping, source modeling
Resource URI on the dog food server: http://data.semanticweb.org/conference/eswc/2012/paper/demonstation/357
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