Web interface to manually annotate named entity mentions in newspaper articles with the correct DBpedia link(s), if any. Produces labeled data sets for training and evaluating the DAC Entity Linker.

To start the web applicaton, run:

./web.py

This will start a Bottle web server listening on http://localhost:5001. To work on a specific data set, add the set name, e.g. tve, to the URL:

http://localhost:5001/tve

The training application will automatically show the first example from the requested set that hasn't been labeled yet.

In order to request a specific example from a data set, use either id (unique identifier) or index (index number within the set) as a parameter:

http://localhost:5001/tve?index=1

Selecting one or more candidates from the user interface as the correct links and navigating to the next example using the menu in the upper right corner will save the selection.

Adding a new article to the set:

http://localhost:5001/tve/edit?action=add&url=http://resolver.kb.nl/resolve?urn=ddd:010734861:mpeg21:a0002:ocr

Adding a specific named entity to the set:

http://localhost:5001/tve/edit?action=add&url=http://resolver.kb.nl/resolve?urn=ddd:010734861:mpeg21:a0002:ocr&ne=Einstein

Removing an article from the set:

http://localhost:5001/tve/edit?action=delete&url=http://resolver.kb.nl/resolve?urn=ddd:010734861:mpeg21:a0002:ocr

All datasets are stored as an art.json file in a folder with the dataset name within the users folder. To create a new, empty dataset named foo:

$ mkdir users/foo
$ echo '{"instances": []}' > users/foo/art.json

The art.json file has to be writeable by the web application and is expected to contain at least an empty list of instances.