Creating Features for Matching

If you have to use supervised learning-based matchers, then you cannot just operate on the labeled set of tuple pairs. For each tuple in the labeled, you need to convert it into a feature vector which consists of a list of numerical/categorical features. To do this, first we need to create a set of features.

There are two ways to create features:

• Automatically create a set of features (then the user can remove or add some more).

• Skip the automatic process and generate features manually.

Creating the Features Manually

This is very similar to manual feature creation process for blocking (see section Creating Features for Blocking) except the features are created for matching purposes. In brief, you can execute the following sequence of commands in py_entitymatching to create the features manually:

>>> match_t = em.get_tokenizers_for_matching()
>>> match_s = em.get_sim_funs_for_matching()
>>> atypes1 = em.get_attr_types(A) # don't need, if atypes1 exists from blocking step
>>> atypes2 = em.get_attr_types(B) # don't need, if atypes2 exists from blocking step
>>> match_c = em.get_attr_corres(A, B)
>>> match_f = em.get_features(A, B, atypes1, atype2, match_c, match_t, match_s)

Further, you can add or delete features as see saw in section Adding/Removing Features.

Please refer to the API reference of get_tokenizers_for_matching() and py_entitymatching.get_sim_funs_for_matching() for more details.

Note

Currently, py_entitymatching returns the same set of features for blocking and matching purposes.

Creating the Features Automatically

If you do not want to go through the hassle of creating the features manually, then the user can generate the features automatically. This is very similar to automatic feature creation process for blocking (see section Generating Features Automatically).

In py_entitymatching, you can use get_features_for_matching to generate features for matching purposes automatically. An example of using get_features_for_matching is shown below:

>>> match_f = em.get_features_for_matching(A, B)

Similar to what we saw in section Generating Features Automatically for blocking, the command will set the following variables: _match_t, _match_s, _atypes1, _atypes2, _match_c and they can be accessed like this:

>>> em._match_t
>>> em._match_s
>>> em._atypes1
>>> em._atypes2
>>> em._match_c

You can to examine these variables, modify them as appropriate, and then perhaps regenerate a set of features. Please refer to the API reference of get_features_for_matching() for more details.