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

More Accurate Question Answering on Freebase
https://github.com/ad-freiburg/aqqu

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More Accurate Question Answering on Freebase

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README 

        
# Aqqu Question Answering System



This is an ancestor of the code accompanying the publication: [More Accurate

Question Answering on Freebase, Hannah Bast and Elmar Haussmann, CIKM 2015](https://ad-publications.cs.uni-freiburg.de/freebase-qa.pdf)



The original version can be found as as the very first commit of this

repository and under the v1.0 tag.



Follow the instructions below to set up the system. This also includes

descriptions on how to obtain pre-requisite data.



Setup is easy if all pre-requisites are met.



## Requirements:



* Docker > 18.09 on 64 bit Linux

* RAM: 40 GB for training the large WebQuestions/WebQSP models

* Disk: about 50 GB for all required data



## Setup a Virtuoso Instance with Freebase



To setup a Virtuoso instance with the Freebase data needed for Aqqu we

recommend to follow the instructions at our  [Virtuoso with Docker

Compose](https://github.com/ad-freiburg/virtuoso-compose) repository. This will

automatically download the correct version of Freebase and setup Virtuoso with

the exact same settings used by us.



## Get the Dataset



All data required for learning can be found under

`/nfs/datastets/aqqu_input_data` when on any of the Chair's computer systems,

all other data is generated automatically.



    cp -r /nfs/datasets/aqqu_input_data/* input/



Outside our system's please contact [Prof. Hannah

Bast](http://ad.informatik.uni-freiburg.de/contact). with the above path to get

a download.



## Train with the provided script

When using docker/wharfer with user namespaces you may need to first run

`chmod o+wX data` so Aqqu running inside docker can write data even if it is

a very restricted user like `nobody`



    ./build_and_run.sh train -n  -r  



## Run with the provided script



    ./build_and_run.sh backend -n  -r  -p  



## Debug changes with the provided script

The `backend` command does not rebuild the image but reuses the exact image

used for training. Therefore changes to the source code made after the train

step are not reflected in the behavior of `backend`.



To try out changes **without affecting the train/backend** image use the debug

command.



    ./build_and_run.sh debug -n  -r  -p  



Once the changes have been tested the image used by the `backend` command can

be updated using. *Note* however that this is somewhat dangerous as it does not

work for changes that would make the model incompatible to the trained model.



    ./build_and_run.sh update -n 



Test the Backend

----------------

The Aqqu backend provides a simple JSON API that can easily be tested using

`curl`. If you have the `./build_and_run.sh backend …` command running on

a server `` with port `` the following asks for Albert Einstein's

place of birth



    curl http://:/?q=where%20was%20albert%einstein%20born



## Run Cross Validation with the provided script



    ./build_and_run.sh cv -n  -r  



## Overriding parameters

To override certain ranker parameters you can use `--override` with a JSON object as additional argument for example

`--override '{"top_ngram_percentile": 15}'`



## Disabling GPU

To disable GPU use run above commands with the environment variable `NO_GPU=1`



## Using (nvidia-)docker directly

To use (nvidia-)docker directly refer to the `build_and_run.sh` script. No

additional documentation is provided as this is discouraged the script should

be reasonably easy to follow.