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Requirements

  • python3
  • rdflib=4.2.2

Note: After installing rdflib via pip or anaconda or some other tools, we need to fix some bugs of it.

First, find your rdflib location. One possible way is to run following codes in ipython

import rdflib
rdflib.__file__

It returns ~/anaconda3/lib/python3.7/site-packages/rdflib/__init__.py in my computer, so I enter the folder ~/anaconda3/lib/python3.7/site-packages/rdflib.

Then open plugins/sparql/parser.py, find Line 68, replace its code with

if i + 1 < l and (not isinstance(terms[i + 1], str) or terms[i + 1] not in ".,;"):

Remember to keep the original indentation. Note that Line 67 is a comment of # is this bnode the subject of more triplets?. If your line number is different from mine, you could locate the target line by this comment.

Finally, open plugins/serializers/turtle.py, find Line 328, change use_plain=True to use_plain=False

  • SPARQLWrapper=1.8.4

Note: When installing SPARQLWrapper with pip, it may automatically install another package keepalive. You can check whether it is in your environment by

pip show keepalive

If it is installed, it will cause some problems when we execute a large number of SPARQL queries. Specifically, the available ports will be used out. So we need to manually disable the keepalive package. It is okay to directly remove it.

pip uninstall keepalive
  • Virtuoso backend, refer to the next section

How to install virtuoso backend

The virtuoso backend will start up a web service, we can import our kb into it and then execute SPARQL queries by network requests. We install virtuoso in an Ubuntu 16.04 system. Following are specific steps.

  1. Download and install virtuoso into our system.
git clone https://github.com/openlink/virtuoso-opensource.git Virtuoso-Opensource
cd Virtuoso-Opensource
git checkout stable/7
sudo apt-get install libtool gawk gperf autoconf automake libtool flex bison m4 make openssl libssl-dev
sudo ./autogen.sh
sudo ./configure
sudo make
sudo make install
  1. Create a new user for virtuoso service
sudo useradd virtuoso --home /usr/local/virtuoso-opensource
sudo chown -R virtuoso /usr/local/virtuoso-opensource
  1. Modify some necessary configs:
cd /usr/local/virtuoso-opensource/var/lib/virtuoso/db
sudo vim virtuoso.ini

Find the item CheckpointInterval, and change its value from default 60 to 0, to avoid automatical checkpoint process which will cause 404 error.

  1. Start up the virtuoso service:
sudo -H -u virtuoso ../../../../bin/virtuoso-t -f &

Now you can access the service via the default port 8890. Enter [ip]:8890 in a browser, you will see the virtuoso service page.

  1. Now we can import our kb into virtuoso. Before that, we need to convert our kb to ttl format and move it to proper position:
python sparql_engine.py --kb_path .dataset/kb.json --ttl_path .dataset/kb.ttl
sudo chmod 777 .dataset/kb.ttl
sudo mv .dataset/kb.ttl /usr/local/virtuoso-opensource/share/virtuoso/vad
  1. Enter the interactive terminal of virtuoso:
cd /usr/local/virtuoso-opensource/bin
sudo ./isql
  1. Import our kb by executing these commands in terminal:
SPARQL CREATE GRAPH <[graph_name]>;
SPARQL CLEAR GRAPH <[graph_name]>;
delete from db.dba.load_list;
ld_dir('/usr/local/virtuoso-opensource/share/virtuoso/vad', 'kb.ttl', '[graph_name]');
rdf_loader_run();
select * from DB.DBA.load_list;
exit;

[graph_name] could be any legal string, such as KQAPro. You are success if rdf_loader_run() lasts for about 10 seconds.

How to run

  1. Follow the last section, start up the virtuoso service and import kb.ttl. Then you need to open sparql_engine.py and find the lines of
virtuoso_address = "http://127.0.0.1:8890/sparql"
virtuoso_graph_uri = 'sjx'

Change virtuoso_address to your service url (you can visit it in your browser to check whether it is valid) and change virtuoso_graph_uri to your <graph_name>. 2. Preprocess the training data

python -m SPARQL.preprocess --input_dir ./dataset --output_dir <dir/of/processed/files>
cp ./dataset/kb.json <dir/of/processed/files>
  1. Train
python -m SPARQL.train --input_dir <dir/of/processed/files> --save_dir <dir/of/checkpoint>
  1. Predict answers of the test set. It will produce a file named predict.txt in the --save_dir, storing the predictions of test questions in order.
python -m SPARQL.predict --input_dir <dir/of/processed/files> --save_dir <dir/of/checkpoint>