ampligraph.datasets.load_wn11(check_md5hash=False, clean_unseen=True, add_reciprocal_rels=False)

Load the WordNet11 (WN11) dataset

WordNet was originally proposed in WordNet: a lexical database for English [Mil95].

WN11 dataset is loaded from file if it exists at the AMPLIGRAPH_DATA_HOME location. If AMPLIGRAPH_DATA_HOME is not set the the default ~/ampligraph_datasets is checked.

If the dataset is not found at either location, it is downloaded and placed in AMPLIGRAPH_DATA_HOME or ~/ampligraph_datasets.

It is divided in three splits:

  • train
  • valid
  • test

Both the validation and test splits are associated with labels (binary ndarrays), with True for positive statements and False for negatives:

  • valid_labels
  • test_labels
Dataset Train Valid Pos Valid Neg Test Pos Test Neg Entities Relations
WN11 110361 2606 2609 10493 10542 38588 11
  • check_md5hash (boolean) – If True check the md5hash of the files. Defaults to False.
  • clean_unseen (bool) – If True, filters triples in validation and test sets that include entities not present in the training set.
  • add_reciprocal_rels (bool) – Flag which specifies whether to add reciprocal relations. For every <s, p, o> in the dataset this creates a corresponding triple with reciprocal relation <o, p_reciprocal, s>. (default: False).

splits – The dataset splits: {‘train’: train, ‘valid’: valid, ‘valid_labels’: valid_labels, ‘test’: test, ‘test_labels’: test_labels}. Each split containing a dataset is an ndarray of shape [n, 3]. The labels are ndarray of shape [n].

Return type:



>>> from ampligraph.datasets import load_wn11
>>> X = load_wn11()
>>> X["valid"][0]
array(['__genus_xylomelum_1', '_type_of', '__dicot_genus_1'], dtype=object)
>>> X["valid_labels"][0:3]
array([ True, False,  True])