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        "description": "RDFSim\n======\n\nThis Python library helps generating a vector space from very large hierarchies encoded in RDF. An obvious example application is to generate a vector space from a SKOS hierarchy or an RDFS subclass hierarchy.\n\nGetting started\n---------------\n\nRunning the tests:\n\n    $ nosetests\n\nInstalling:\n \n    $ python setup.py install\n\nExample use\n-----------\n\n    $ wget http://downloads.dbpedia.org/3.7/en/skos_categories_en.nt.bz2\n    $ bunzip2 skos_categories_en.nt.bz2\n    $ python\n    >>> from rdfsim.space import Space\n    >>> space = Space('skos_categories_en.nt')\n    >>> space.similarity_uri(category1, category2)\n\nConstructing a vector space for the entire DBpedia SKOS category\nhierarchy (3M triples) takes a couple of minutes on a commodity laptop, \nand has a memory footprint of about 500M.\n\nAlternatively, a subset of it is available in the examples/ directory.\n\nHow it works\n------------\n\nFor each topic t in the hierarchy, we consider the set of its parents\nparents(t, k) at a level k. We construct a vector for each t in a space\nwhere each dimension corresponds to a topic d in the hierarchy. The value\nof t on dimension d is defined as follows:\n\n    t_d = \\sum_{k = 0}^{max_depth} \\sum_{d \\in parents(t, k)} decay^k\n\nwhere max_depth and decay are two parameters, which can be used to influence\nhow much importance we attach to ancestors that are high in the\ncategory hierarchy.\n\nThey can be specified as follows:\n\n    >>> Space.max_depth = 8\n    >>> Space.decay = 0.9\n\nLicensing terms and authorship\n------------------------------\n\nSee 'COPYING' and 'AUTHORS' files.\n",
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