Aqqu

Description

Question answering from Freebase as described in the CIKM 2015 publication. The code below also contains some improvements (neural network, performance) that came after the publication. The public code also contains a README that describes how to download, install, train and run the system. Below describes how to setup the demo (for which the code is not public).

Code

The main repository is public on GitHub:

https://github.com/ad-freiburg/aqqu

There is also an internal repository that should be kept in sync with the GitHub one but may contain branches for internal testing/conferences:

https://ad-git.informatik.uni-freiburg.de/ad/Aqqu

The Web UI is currently only available internally. It should probably made public too:

https://ad-git.informatik.uni-freiburg.de/ad/aqqu-webserver

Previously Elmar kept an internal Aqqu version on BitBucket this is very old and stale though:

https://bitbucket.org/elmar-haussmann/aqqu .

Similarly there is an old BitBucket repository for the Web UI:

https://bitbucket.org/elmar-haussmann/aqqu-webserver .

Instances

The recommended way to set up an Aqqu instances is described in the README on GitHub and uses nvidia-docker (or docker when not using a GPU).

The below instances were set up be me (Niklas) so they might need a sudo -u schnelle -i before to get the right permissions for the repositories.

This method is used on the following Aqqu instances:

• On titan (http://titan.informatik.privat:8300)

  • Using the QLever SPARQL Engine on fiji listed here:

  • Under /local/ssd2/aqqu

• On vulcano (http://vulcano.informatik.privat:8090):

  • First trained on the sqtrain dataset then on wqsptrain and free917train

  • Under /local/raid/ad/schnelle/aqqu

  • Using the Virtuoso SPARQL Engine docker-compose setup

    • docker-compose up -d in /local/raid/ad/schnelle/virtuoso-compose

    • https://github.com/ad-freiburg/virtuoso-compose

These instances can be used with the following Aqqu Web UI instances

• On titan (http://titan.informatik.privat:5454, http://titan.informatik.privat:5455 )

  • Under /local/ssd2/aqqu_webui/

  • Launched with a single docker-compose up -d --scale webui=2

Old Aqqu instance (via Elmar's home)

Start as follows on metropolis:

ssh metropolis
sudo su haussmae
cd /home/haussmae/demos/aqqu-demo
source venv/bin/activate   # was: activate aqqu
PYTHONPATH=$(pwd):$PYTHONPATH python webserver/translation_webserver.py

Virtuoso instance (using Docker Compose)

2018-04-02: Virtuoso instance for Aqqu runs at http://vulcano.informatik.privat:9000/sparql this is proxied to http://aqqu.informatik.uni-freiburg.de/sparql/

First make sure you have Docker + Docker Compose set up as described here: https://docs.docker.com/compose/install/ also make sure that your user is in the *docker group*

If you are setting up a new instance first check out the repository. If this is an existing instance just switch to its checked out virtuoso-compose (e.g. at vulcano:/local/raid/ad/schnelle/virtuoso-compose)

git clone https://ad-git.informatik.uni-freiburg.de/ad/virtuoso-compose.git
cd virtuoso-compose

Then start virtuoso with varnish as a caching proxy as follow (the resulting server listens on port 9000). Note that this is the same command whether you set up initially or manually launch the instance

docker-compose up -d

To view the log run

docker-compose logs -f

Virtuoso instance (via Elmars home)

Start virtuoso as follows. Server listens on port 8999. (Alternatively follow the instructions from scratch instructions below)

ssh metropolis
sudo su haussmae
cd /home/haussmae/keyword-translation
make start-virtuoso-mini-cai

Start the HTTP proxy as follows (kill old process manually, listens on port 9000):

ssh metropolis
sudo su haussmae
cd /home/haussmae/keyword-translation
make start-varnish

Parser

Aqqu uses a parser to get part-of-speech tags of query words. The parser is accessed via HTTP Api calls. To start the parser server:

ssh metropolis
sudo su haussmae
cd /home/haussmae/keyword-translation
make start-parser

The port is configured in the corenlp-frontent/build.xml. The API can be accessed like this: http://metropolis.informatik.uni-freiburg.de:4000/parse/?text=This%20is%20a%20test%20sentence.

Run the new Aqqu version (with NN)

Start on titan (requires GPU):

ssh titan
sudo su haussmae
cd /home/haussmae/aqqu-bitbucket
source activate aqqu
PYTHONPATH=$(pwd):$PYTHONPATH python webserver/translation_webserver.py

Runs on port 5454 on titan now. However, titan is not available from outside the uni network.

To start a port-forwarding from metropolis:

ssh metropolis
sudo su haussmae
cd /home/haussmae/temp/nc/python-port-forwardt
python2 port-forward.py

The service is now available on metropolis:5454

How to update (any) Virtuoso with custom data

Grant access rights via the ISQL tool as follows:

data/virtuoso/install/bin/isql localhost:1112 dba dba
grant execute on SPARQL_INSERT_DICT_CONTENT to SPARQL_UPDATE;
grant execute on SPARQL_INSERT_DICT_CONTENT to "SPARQL";
grant execute on SPARQL_DELETE_DICT_CONTENT to SPARQL_UPDATE;
grant execute on SPARQL_DELETE_DICT_CONTENT to "SPARQL";

Complex example SPARQL query from "Programmieren in C++, SS 2016, Ü10 (all action or animation movie with their release date, genre, director, production company, and rating):

PREFIX fb: <http://rdf.freebase.com/ns/>
 SELECT DISTINCT ?fn, ?y, ?gn, ?dn, ?pn, ?rn where {
 ?f fb:type.object.type fb:film.film .
 ?f fb:film.film.initial_release_date ?y .
 ?f fb:film.film.genre ?g .
 ?f fb:film.film.directed_by ?d .
 ?f fb:film.film.production_companies ?p .
 ?f fb:film.film.rating ?r .
 ?f fb:type.object.name ?fn .
 ?g fb:type.object.name ?gn .
 ?d fb:type.object.name ?dn .
 ?p fb:type.object.name ?pn .
 ?r fb:type.object.name ?rn
 FILTER(lang(?fn)='en')
 FILTER(lang(?gn)='en')
 FILTER(lang(?dn)='en')
 FILTER(lang(?pn)='en')
 FILTER(lang(?rn)='en')
 FILTER(?gn='Action Film'@en OR ?gn='Animation'@en)
}

Data

All of the required data to run Aqqu is part of the materials (see above). It is located in the data subfolder. The scripts to create this data are part of the (old) keyword-translation repository: https://bitbucket.org/onekonek/keyword-translation