Live Demo

You can try this feature with your own data in this page.

Tip

To obtain geographic information for your dataset, follow these steps:

1. Click on the “rocket icon” located in the top-right corner.

2. Select the option labeled Live Code from the menu.

3. Once the environment is launched, you’ll be able to manually execute each code cell.

For any hidden code cells, simply click on Show code cell source and subsequently click run within each respective cell section.

Here’re the information you may input:

• title: the title of your dataset

• description: description of your dataset

• resource_names: file names in your dataset

• resource_descriptions: the description of files in your dataset

• organization_title: the tilte of the affiliatedorganization

• organization_description: the description of the affiliatedorganization

Warning

Notice At least one of the fields should be completed. Leaving all of them empty is not permissible.

Function Definations

Just expend it and click run

Show code cell source Hide code cell source

# Packages Import ============================================================
import requests

# NLP task model
from ckip_transformers.nlp import CkipNerChunker
ner_driver = CkipNerChunker(model="bert-base")

# Function Definstion =========================================================
def search_osm_place(query):
    base_url = "https://nominatim.openstreetmap.org/search"
    params = {
        "q": query,
        "format": "json",
        "polygon_geojson": "1",  # Request GeoJSON polygons
        "limit": 7
    }

    response = requests.get(base_url, params=params)

    if response.status_code == 200:
        return response.json()
    else:
        return None

def make_keyword_map(input_list):
    # NER task
    ner = ner_driver(input_list)

    # Build keyword_map to store potential words
    avoid_class = ['QUANTITY', 'CARDINAL', 'DATE', 'ORDINAL']
    keyword_map = {}
    for sentence_ner in ner:
        for entity in sentence_ner:
            if(entity[1] in avoid_class):
                continue
        keyword_map[entity[0]] = entity[1]
    
    return keyword_map

def gen_keyword(title, description, resource_names, resource_descriptions, organization_title, organization_description):
    input_list = [title, description, resource_names, resource_descriptions, organization_title, organization_description]

    if all(not item for item in input_list):
        return -1
    else:
       keyword_map = make_keyword_map(input_list)
       return keyword_map

def output(result):
    if(result == -1):
        print("At least one of the fields should be completed. Leaving all of them empty is not permissible.")
        return
    else:
        for item in result:
            respond = search_osm_place(item)
            if respond:
                print(f"OSM result for {item} is:")
                for place in respond:
                    print("📍", place["display_name"])
                    print(str(place["geojson"]).replace("'", "\""))
                print('-------------------------------------------')
            else:
                    print("No geoInfo provided.")

Input

✨ You can type information of your own dataset here:

title = ''
description = ''
resource_names = []
resource_descriptions = []
organization_title = ""
organization_description = ""

result = gen_keyword(title, description, resource_names, resource_descriptions, organization_title, organization_description)

Output

Below’s our recomnned wikidata keyword(s) for your dataset:

output(result)

At least one of the fields should be completed. Leaving all of them empty is not permissible.

Preview

Here you can select one of the geographic information, input it below to preview this location:

geoInfo = ''

import folium

center_coords = [25.041415686746607, 121.61472689731077]  # Sinica
m = folium.Map(location=center_coords, zoom_start=12)

if(len(geoInfo) == 0):
    print("please paste the geoJSON in the 'geoInfo' string.")
else:
    geojson = eval(geoInfo)
    folium.GeoJson(geojson).add_to(m)
    m.fit_bounds(m.get_bounds())
    display(m)

please paste the geoJSON in the 'geoInfo' string.