cd backendCreate a virtual environment 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):
pip install -r requirements.txtDownload 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.
cd ..
git clone git@github.com:usc-isi-i2/data-label-augmentation.gitYou now need to install that project's requirements into the virtual environment
cd data-label-augmentation/src/label-augmenter
pip install -r requirements.txtUpdate cache:
python cache.pyUpdate indices:
python update_index.pyStart web service
python ws.pytime 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
}
]
}
]