@@ -9,7 +9,8 @@ patterns. Given a source, the learned patterns will try to lead you to the right
99target.
1010
1111The algorithm was first developed on a list of human associations that had been
12- mapped to DBpedia entities, as can be seen in [ data/gt_associations.csv] :
12+ mapped to DBpedia entities, as can be seen in
13+ [ data/gt_associations.csv] ( ./data/gt_associations.csv ) :
1314
1415| source | target |
1516| --------------------------------- | --------------------------------- |
@@ -27,15 +28,15 @@ your list of source-target-pairs is less complicated, the algorithm will happily
2728terminate earlier.
2829
2930You can find more information about the algorithm and learning patterns for
30- human associations on [ https://w3id.org/associations ] . The page also includes
31+ human associations on https://w3id.org/associations . The page also includes
3132publications, as well as the resulting patterns learned for human associations
3233from a local DBpedia endpoint including wikilinks.
3334
3435
3536Installation
3637------------
3738
38- Currently the suggested installation method is via git clone (also allows easier
39+ For now, the suggested installation method is via git clone (also allows easier
3940contributions):
4041
4142 git clone git@github.com:RDFLib/graph-pattern-learner.git
@@ -106,3 +107,9 @@ patterns generalise, you can run:
106107The script will then first learn patterns, visualise them in
107108` ./results/bundle_name/visualise ` , before evaluating predictions on first the
108109training- and then the test-set.
110+
111+
112+ Contributors
113+ ------------
114+ * Jörn Hees
115+ * Rouven Bauer (visualise code)
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