is a thin python wrapper around DBpedia Spotlight's REST Interface.

The currently supported DBpedia Spotlight versions are 0.5 and 0.6.5. However, as long as there are no major API overhauls, this wrapper might also work with future versions. If you encounter a bug with a newer DBpedia version, feel free to create an issue here on github.

Note that I'm trying to track DBpedia Spotlight release version numbers, so you can easily see which pyspotlight version has been tested with which Spotlight release. Therefore all pyspotlight 0.5 releases are tested against Spotlight 0.5.

The newest stable release can be found on the Python Package Index (PyPi).

Therefore installation is as easy as:

pip install pyspotlight

This module has been tested with Python 2.6 and Python 2.7.

As long as you use the setup.py for the installation (python setup.py install), you'll be fine because Python takes care of the dependencies for you.

If you decide not to use the setup.py you will need the requests library. In case you are running a Python Version older than 2.7, you will also need to install the ordereddict module.

All of these packages can be found on the Python PackageIndex and easily installed via either easy_install or, the recommended, pip.

Using pip it is especially easy because you can just do this:

pip install -r requirements.txt

and it will install all packages from that file.

if you just want to play around with spotlight, there is a running version available under http://spotlight.dbpedia.org/rest/annotate.

Usage is simple and easy, just as is the API:

>>> import spotlight
>>> annotations = spotlight.annotate('http://localhost/rest/annotate',
...                                  'Your test text',
...                                  confidence=0.4, support=20)

This should return a list of all resources found within the given text. Assuming we did this for the following text:

President Obama on Monday will call for a new minimum tax rate for individuals making more than $1 million a year to ensure that they pay at least the same percentage of their earnings as other taxpayers, according to administration officials.

We might get this back:

>>> annotation
[{u'URI': u'http://dbpedia.org/resource/Presidency_of_Barack_Obama',
  u'offset': 0,
  u'percentageOfSecondRank': -1.0,
  u'similarityScore': 0.10031112283468246,
  u'support': 134,
  u'surfaceForm': u'President Obama',
  u'types': u'DBpedia:OfficeHolder,DBpedia:Person,Schema:Person,Freebase:/book/book_subject,Freebase:/book,Freebase:/book/periodical_subject,Freebase:/media_common/quotation_subject,Freebase:/media_common'},…(truncated remaining elements)…]

The same parameters apply to the spotlight.candidates function.

The following exceptions can occur:

• ValueError when:

  • the JSON response could not be decoded.

• SpotlightException when:

  • the JSON response did not contain any needed fields or was not formed as excepted.
  • You forgot to explicitly specify a protocol (http/https) in the API URL.

  Usually the exception's message is telling you exactly what is wrong. If not, I might have forgotten some error handling. So just open up an issue on github.

• requests.exceptions.HTTPError

  Is thrown when the response http status code was not 200. This could happen if you have a load balancer like nginx in front of your spotlight cluster and there is not a single server available, so nginx throws a 502 Bad Gateway.

Note that the API also supports a disambiguate interface, however I wasn't able to get it running. Therefore there is no disambiguate function available. Feel free to contribute :-)!

I'd highly recommend playing around with the confidence and support values. Furthermore it might be preferable to filter out more annotations by looking at their smiliarityScore (read: contextual score).

If you want to change the default values, feel free to use itertools.partial to create a little wrapper with simplified signature:

>>> from spotlight import annotate
>>> from functools import partial
>>> api = partial(annotate, 'http://localhost/rest/annotate',
...               confidence=0.4, support=20,
...               spotter='AtLeastOneNounSelector')
>>> api('This is your test text. This function has other confidence,
...      support and uses another spotter. Furthermore all calls go
...      directl to localhost/rest/annotate.')

As you can see this reduces the function's complexity greatly. I did not feel the need to create fancy classes, they would've just lead to more complexity.

If you want to run the tests, you will have to install nose (1.2.1) from the package index. Then you can simply run nosetests from the command line in this or the spotlight/ directory.

In case you spot a bug, please open an issue and attach the raw response you sent. Have a look at Issue #3 for a great example on how to file a bug report.