cd backend
Create a virtual environment in the env directory and install the requirements (on Windows, don't use Python 3.8 yet - as of January 2020, since not all packages have precompiled binary wheels. Python 3.7 works fine. Replace py with python3 if you don't have the Python Launcher installed).
py -m venv env --prompt="wikidata"
pip install -r requirements.txt
Download word2vec model: GoogleNews-vectors-negative300-SLIM.bin, unzip and place it in backend/data-label-augmentation/data/GoogleNews-vectors-negative300-SLIM.bin.
You need to clone the Data Label Augmenter repository to a side folder of this project. The repository is private, so you need to get permission to do that - without it you cannot run the backend.
So, if you are in the root directory of this repository (the directory containing this file), do
cd ..
git clone [email protected]:usc-isi-i2/data-label-augmentation.git
You now need to install that project's requirements into the virtual environment (the same virtual environment you have created before)
cd data-label-augmentation/src/label-augmenter
pip install -r requirements.txt
The backend has a settings.py file that contains reasonable default. To override these settings at a local_settings.py file. You probably shouldn't change the settings if you are not deploying the backend.
The frontend has a .env file that also contains reasonable defaults.
You need to update the cache and indices for the backend to work. This is simple. From the backend folder run
python cache.py
If you want to refresh the cache and indices add --reload
Start web service
python ws.py
We have provided tasks and debugger launch configurations for Visual Studio Code. Unfortunately the settings file depends on your OS - Windows or Linux (Macs are considered Linux). We have provided two
sample settings files, .vscode/settings.linux.json and .vscode/settings.windows.json . Please copy the appropriate one into .vscode/settings.json, then start Visual Studio Code.
If you are using PyCharm, you will need to add the backend folder, as well as ../data-label-augmentation/src/label-augmenter to the PYTHON PATH.
time is null if no qualifier of that property for that country can be found.
statistics only appears when time is not null. In statistics, max_precision is the most precise precision, it can be null if none of the time value has precision.
> curl -s "localhost:14000/linking/wikidata?keywords=population&country=Q30" | jq .
[
{
"keyword": "population",
"augmentation": {
"population": 1,
"populations": 1,
"populace": 1,
"inhabitants": 1,
"populaton": 1,
"populous": 1,
"landmass": 1,
"census": 1
},
"alignments": [
{
"name": "P1082",
"label": "population",
"description": "number of people inhabiting the place; number of people of subject",
"aliases": [
"human population",
"inhabitants"
],
"time": "P585",
"qualifiers": {
"P585": "point in time",
"P459": "determination method",
"P1539": "female population",
"P1540": "male population"
},
"statistics": {
"max_time": "2017-01-01T00:00:00Z",
"min_time": "1790-01-01T00:00:00Z",
"count": 52,
"max_precision": 11
},
"score": 0.1271124601458984
},
{
"name": "P6499",
"label": "literate population",
"description": "number of literate people within a territorial entity (typically determined during a census)",
"aliases": [],
"time": null,
"qualifiers": {},
"score": 0.09306288181984021
},
{
"name": "P111322",
"label": "Population ages 05-09, female (% of female population)",
"description": "Female population between the ages 5 to 9 as a percentage of the total female population.",
"aliases": [],
"time": "P585",
"qualifiers": {
"P275": "license",
"P585": "point in time",
"P248": "stated in"
},
"statistics": {
"max_time": "2018-01-01T00:00:00Z",
"min_time": "1960-01-01T00:00:00Z",
"count": 59,
"max_precision": 9
},
"score": 0.07329026879389923
},
{
"name": "P111324",
"label": "Population ages 10-14, female (% of female population)",
"description": "Female population between the ages 10 to 14 as a percentage of the total female population.",
"aliases": [],
"time": "P585",
"qualifiers": {
"P275": "license",
"P585": "point in time",
"P248": "stated in"
},
"statistics": {
"max_time": "2018-01-01T00:00:00Z",
"min_time": "1960-01-01T00:00:00Z",
"count": 59,
"max_precision": 9
},
"score": 0.07329026879389923
},
{
"name": "P111354",
"label": "Population ages 80 and above, female (% of female population)",
"description": "Female population between the ages 80 and above as a percentage of the total female population.",
"aliases": [],
"time": "P585",
"qualifiers": {
"P275": "license",
"P585": "point in time",
"P248": "stated in"
},
"statistics": {
"max_time": "2018-01-01T00:00:00Z",
"min_time": "1960-01-01T00:00:00Z",
"count": 59,
"max_precision": 9
},
"score": 0.07329026879389923
},
{
"name": "P111319",
"label": "Population ages 00-04, female (% of female population)",
"description": "Female population between the ages 0 to 4 as a percentage of the total female population.",
"aliases": [],
"time": "P585",
"qualifiers": {
"P275": "license",
"P585": "point in time",
"P248": "stated in"
},
"statistics": {
"max_time": "2018-01-01T00:00:00Z",
"min_time": "1960-01-01T00:00:00Z",
"count": 59,
"max_precision": 9
},
"score": 0.07329026879389923
},
{
"name": "P111341",
"label": "Population ages 50-54, female (% of female population)",
"description": "Female population between the ages 50 to 54 as a percentage of the total female population.",
"aliases": [],
"time": "P585",
"qualifiers": {
"P275": "license",
"P585": "point in time",
"P248": "stated in"
},
"statistics": {
"max_time": "2018-01-01T00:00:00Z",
"min_time": "1960-01-01T00:00:00Z",
"count": 59,
"max_precision": 9
},
"score": 0.07329026879389923
},
{
"name": "P111343",
"label": "Population ages 55-59, female (% of female population)",
"description": "Female population between the ages 55 to 59 as a percentage of the total female population.",
"aliases": [],
"time": "P585",
"qualifiers": {
"P275": "license",
"P585": "point in time",
"P248": "stated in"
},
"statistics": {
"max_time": "2018-01-01T00:00:00Z",
"min_time": "1960-01-01T00:00:00Z",
"count": 59,
"max_precision": 9
},
"score": 0.07329026879389923
},
{
"name": "P111350",
"label": "Population ages 70-74, female (% of female population)",
"description": "Female population between the ages 70 to 74 as a percentage of the total female population.",
"aliases": [],
"time": "P585",
"qualifiers": {
"P275": "license",
"P585": "point in time",
"P248": "stated in"
},
"statistics": {
"max_time": "2018-01-01T00:00:00Z",
"min_time": "1960-01-01T00:00:00Z",
"count": 59,
"max_precision": 9
},
"score": 0.07329026879389923
},
{
"name": "P111352",
"label": "Population ages 75-79, female (% of female population)",
"description": "Female population between the ages 75 to 79 as a percentage of the total female population.",
"aliases": [],
"time": "P585",
"qualifiers": {
"P275": "license",
"P585": "point in time",
"P248": "stated in"
},
"statistics": {
"max_time": "2018-01-01T00:00:00Z",
"min_time": "1960-01-01T00:00:00Z",
"count": 59,
"max_precision": 9
},
"score": 0.07329026879389923
}
]
}
]