get_relation_pattern_types_df(dataset, *, min_support=0, min_confidence=0.95, drop_confidence=False, parts=None, force=False, add_labels=True)[source]

Categorize relations based on patterns from RotatE [sun2019].

The relation classifications are based upon checking whether the corresponding rules hold with sufficient support and confidence. By default, we do not require a minimum support, however, a relatively high confidence.

The following four non-exclusive classes for relations are considered:

  • symmetry

  • anti-symmetry

  • inversion

  • composition

This method generally follows the terminology of association rule mining. The patterns are expressed as

\[X_1 \land \cdot \land X_k \implies Y\]

where \(X_i\) is of the form \(r_i(h_i, t_i)\), and some of the \(h_i / t_i\) might re-occur in other atoms. The support of a pattern is the number of distinct instantiations of all variables for the left hand side. The confidence is the proportion of these instantiations where the right-hand side is also true.

  • dataset (Dataset) – The dataset to investigate.

  • min_support (int) – A minimum support for patterns.

  • min_confidence (float) – A minimum confidence for the tested patterns.

  • drop_confidence (bool) – Whether to drop the support/confidence information from the result frame, and also drop duplicates.

  • parts (Optional[Collection[str]]) – Only use certain parts of the dataset, e.g., train triples. Defaults to using all triples, i.e. {“training”, “validation”, “testing}.

  • force (bool) – Whether to enforce re-calculation even if a cached version is available.

  • add_labels (bool) – Whether to add relation labels (if available).

Return type:



If you intend to use the relation categorization as input to your model, or hyper-parameter selection, do not include testing triples to avoid leakage!

Return type:



A dataframe with columns {“relation_id”, “pattern”, “support”?, “confidence”?}.