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: emor...@nd.edu w: cds.library.nd.edu