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Official TagMe API wrapper for Python

Project description

Official TagMe API wrapper for Python.

Installation and setup

This library is hosted by PyPI. You can install it with:

pip install tagme

To access the TagMe API you have to register (for free!) at the D4Science platform and obtain an authorization token.

  • Register to the D4Science TagMe VRE.
  • After login, click the show button on the left panel to get your authorization token.

Using TagMe

Before making any call to the web service, you will need to set the module-wise GCUBE_TOKEN variable. You can do so with:

import tagme
# Set the authorization token for subsequent calls.
tagme.GCUBE_TOKEN = "<Your token goes here>"

As an alternative to setting the module-wise variable, you can pass the token at each call with the optional gcube_token parameter.


The annotation service lets you find entities mentioned in a text and link them to Wikipedia. This is the so-called Sa2KB problem. You can annotate a text with:

lunch_annotations = tagme.annotate("My favourite meal is Mexican burritos.")

# Print annotations with a score higher than 0.1
for ann in lunch_annotations.get_annotations(0.1):
    print ann

The annotate method accepts parameters to set the language (parameter lang, that defaults to en) and other stuff. See the code for more information. Annotations are associated a rho-score indicating the likelihood of an annotation being correct. In the example, we discard annotations with a score lower than 0.1.

Mention finding

The mention finding service lets you find what parts of text may be a mention of an entity, without linking them to any entity.

tomatoes_mentions = tagme.mentions("I definitely like ice cream better than tomatoes.")

for mention in tomatoes_mentions.mentions:
    print mention

The mentions parameter accepts an optional language parameter lang that defaults to en.

Entity relatedness

Tagme also gives you the semantic relatedness among pairs of entities. Entities can be either specified as Wikipedia titles (like Barack Obama) or as Wikipedia IDs (like 534366, the ID of the entity Barack Obama). The two methods for obtaining the relatedness among entities are relatedness_title (that accepts titles) and relatedness_wid (that accepts Wikipedia IDs). Both methods accept either a single pair of entities or a list of pairs. You can submit a list of pairs of any size, but the TagMe web service will be issued one query every 100 pairs. If one entity does not exist, the result will be None.

# Get relatedness between a pair of entities specified by title.
rels = tagme.relatedness_title(("Barack Obama", "Italy"))
print "Obama and italy have a semantic relation of", rels.relatedness[0].rel

# Get relatedness between a pair of entities specified by Wikipedia ID.
rels = tagme.relatedness_wid((31717, 534366))
print "IDs 31717 and 534366 have a semantic relation of ", rels.relatedness[0].rel

# Get relatedness between three pairs of entities specified by title.
# The last entity does not exist, hence the value for that pair will be None.
rels = tagme.relatedness_title([("Barack_Obama", "Italy"),
                                ("Italy", "Germany"),
                                ("Italy", "BAD ENTITY NAME")])
for rel in rels.relatedness:
    print rel

# You can also build a dictionary
rels_dict = dict(rels)
print rels_dict[("Barack Obama", "Italy")]


See the Changelog.

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