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Since each interaction model has a different range (e.g., the values of some interaction functions could be between [-inf,inf], some could be [0, inf], some could be [0, 1]), it makes the interpretation difficult. Even worse, this also has to do with interaction models' constrains on embeddings, etc.
It would be good for all interaction functions to define an "interpretation function" that remaps the results onto the range of [0,1]
Obtaining such is an active research area and neither straight-forward, nor exists a "unique" solution. In case you are interested, you may want to look for keywords "model calibration" (e.g. Platt scaling), and for KG "triple classification".
The text was updated successfully, but these errors were encountered:
Since each interaction model has a different range (e.g., the values of some interaction functions could be between [-inf,inf], some could be [0, inf], some could be [0, 1]), it makes the interpretation difficult. Even worse, this also has to do with interaction models' constrains on embeddings, etc.
It would be good for all interaction functions to define an "interpretation function" that remaps the results onto the range of [0,1]
From @mberr:
The text was updated successfully, but these errors were encountered: