Knowledgelinks.io BIBCAT

Installation

The easiest way to get started with bibcat is to install with pip:

pip install bibcat

Development installation

You can also clone this repository and run pip from the same directory:

git clone https://github.com/KnowledgeLinks/rdfw-bibcat bibcat
cd bibcat
pip install -e . 

If you don't have pip available (although you should because bibcat is targeted for Python 3.5+) you can also follow the same steps to clone bibcat but run python setup.py install.

Basic Usage

Running RDF Map Processor

RDF Mapping Language RDF turtle files provides the mapping between different input sources including XML, JSON, CSV files, and SPARQL endpoints that we current normalize to a production BIBFRAME 2.0 level of description that also combines other RDF vocabularies for
RDF-based applications.

MARC XML to BIBFRAME 2.0 to Production BIBFRAME 2.0

>>> import lxml.etree
>>> marc2bibframe_xsl = lxml.etree.parse("{path}/marc2bibframe2/xsl/marc2bibframe2.xsl")
>>> xsl_transform = lxml.etree.XSLT(marc2bibframe_xsl)
>>> def bibframe_handler(raw_marc_xml):
    marc_xml = lxml.etree.XML(raw_marc_xml)
    bf_xml = xsl_transform(marc_xml, baseuri="'https://bibcat.org/'")
    bf_rdf = rdflib.Graph().parse(data=lxml.etree.tostring(bf_xml))
    return bf_rdf

MODS XML to Production BIBFRAME 2.0

>>> import uuid
>>> import lxml.etree
>>> from bibcat.rml.processor import processor
>>> base_url = "https://bibcat.org"
>>> mods_xml = lxml.etree.parse("/filepath/to/mods/file.xml")
>>> mods2bf = processor.XMLProcessor(
        base_url=base_url,
        triplestore_url="http://localhost:9999/blazegraph/sparql",
        rml_rules=['bibcat-base.ttl', 'mods-to-bf.ttl'])
>>> mods2bf.run(mods_xml, 
               instance_iri="{}/{}".format(base_url, uuid.uuid1()),
               item_iri="{}/test-item".format(base_url))
>>> mods2bf.output # RDF Graph of BF

Dublin Core XML to Production BIBFRAME 2.0

>>> import uuid, lxml.etree

Production BIBFRAME 2.0 to Schema.org RDF

Production BIBFRAME 2.0 to DP.LA MAPv4

Running Islandora, ContentDM&rep;, and Luna&rep; OAI-Harvester