How might I exploit & learn from a set of RDF files harvested from DOI's?
For a good time, I have written a suite of software to harvest bibliographic data from Web of Science, cache the results, and report on the whole. [1] Along the way I programmatically collect DOI's and then resolve them. The results include RDF streams. ("Thanks, Kevin Ford!") For example:
curl -i -L -H "Accept: application/rdf+xml" http://dx.doi.org/10.3352/jeehp.2013.10.3
And:
<rdf:RDF
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:j.0="http://purl.org/dc/terms/"
xmlns:j.1="http://prismstandard.org/namespaces/basic/2.1/"
xmlns:owl="http://www.w3.org/2002/07/owl#"
xmlns:j.2="http://purl.org/ontology/bibo/"
xmlns:j.3="http://xmlns.com/foaf/0.1/">
<rdf:Description rdf:about="http://dx.doi.org/10.3352/jeehp.2013.10.3">
<j.0:isPartOf>
<j.2:Journal rdf:about="http://id.crossref.org/issn/1975-5937">
<owl:sameAs>urn:issn:1975-5937</owl:sameAs>
<j.0:title>Journal of Educational Evaluation for Health Professions</j.0:title>
<j.1:issn>1975-5937</j.1:issn>
<j.2:issn>1975-5937</j.2:issn>
</j.2:Journal>
</j.0:isPartOf>
<j.0:creator>
<j.3:Person rdf:about="http://id.crossref.org/contributor/sun-huh-112veziy3vi1o">
<j.3:name>Sun Huh</j.3:name>
<j.3:familyName>Huh</j.3:familyName>
<j.3:givenName>Sun</j.3:givenName>
</j.3:Person>
</j.0:creator>
<j.0:title>Revision of the instructions to authors to require... </j.0:title>
<j.1:doi>10.3352/jeehp.2013.10.3</j.1:doi>
<j.0:date rdf:datatype="http://www.w3.org/2001/XMLSchema#date"
>2013-04-30</j.0:date>
<owl:sameAs rdf:resource="info:doi/10.3352/jeehp.2013.10.3"/>
<j.0:identifier>10.3352/jeehp.2013.10.3</j.0:identifier>
<j.2:volume>10</j.2:volume>
<j.2:pageStart>3</j.2:pageStart>
<j.1:startingPage>3</j.1:startingPage>
<j.0:publisher>XMLArchive</j.0:publisher>
<owl:sameAs rdf:resource="doi:10.3352/jeehp.2013.10.3"/>
<j.1:volume>10</j.1:volume>
<j.2:doi>10.3352/jeehp.2013.10.3</j.2:doi>
</rdf:Description>
</rdf:RDF>
That's a pretty rich RDF stream! [2]
As of right now, I have about 8000 of these streams representing publications of faculty here at my university. I can easily get 10's of thousands more. How might I take advantage of this data? How can I go beyond parsing the RDF with XPath, stuffing the results into a database, and applying SQL to the result? How can truly exploit the nature of the RDF and possibly manifest it as linked data?
To answer my own question, I might put the data into a triple store, and then try to answer questions such as: what authors are central, what journals are central, what authors are "related" to whom, etc.
What do you think?
[1] https://github.com/ericleasemorgan/api-taskforce
[2] And this rich data does not even take into account the cool, sometimes full text URLs/URIs found in the HTTP link header!
--
Eric Lease Morgan
Digital Initiatives Librarian, Navari Family Center for Digital Scholarship
Hesburgh Libraries
University of Notre Dame
250E Hesburgh Library
Notre Dame, IN 46556
o: 574-631-8604
e: [log in to unmask]
w: cds.library.nd.edu
