Within the Flemish Government, there is a central governing body that coordinates matters on Information and ICT policies. Under the umbrella of this body, several taskforces exist, one of them being the OSLO Datastandards taskforce. This taskforce creates standards, semantic models and tooling for Flemish government agencies and other connected partners. One of the projects that ran in 2018, was the creation of a Generic Hypermedia API standard, a set of guidelines and building blocks that aim to transform REST services into more Hypermedia driven services. This would make them more interoperable. The first version of this standard was approved on november 8, 2018 and contains three building blocks: language, CRUD and pagination.
At Flanders Heritage, we have started aligning our information systems with this new standard, especially the pagination building block. For the moment we are striving towards adopting the HTTP Link header method of pagination, as specified by https://github.com/Informatievlaanderen/generieke-hypermedia-api/blob/master/paginering.md#http and detailed in https://tools.ietf.org/html/rfc5988.
If we want to see this in action, we need to look at the Link http header. There are several ways to do this. You can use a commandline tool such as cURL, a web browser plugin or a standalone REST client. Whichever one you use, should be able to show you this information. In our examples we’ll be using cURL, a tool that is generally present or easy to install on a Linux or Mac computer.
Suppose we are interested in books and documents about Knokke, a coastal town in Flanders (Hi mom!). We can learn to do this by looking at the REST api docs at https://bib.onroerenderfgoed.be/api_docs. This teaches us that we can call https://bib.onroerenderfgoed.be/werken with the titel parameter. So, https://bib.onroerenderfgoed.be/werken?titel=knokke gives us books that contain Knokke in the title or have a chapter that contains it in the title:
$ curl -G -i -H Accept:application/json https://bib.onroerenderfgoed.be/werken?titel=knokke
HTTP/1.1 200 OK
Date: Mon, 16 Dec 2019 19:51:07 GMT
Server: Apache
Content-Type: application/json
Content-Length: 3933
Access-Control-Expose-Headers: Content-Range, X-Content-Range
Accept-Ranges: items
Content-Range: items 0-9/167
Link: <https://bib.onroerenderfgoed.be/werken?titel=knokke&per_pagina=10&pagina=2>; rel="next", <https://bib.onroerenderfgoed.be/werken?titel=knokke&per_pagina=10&pagina=17>; rel="last", <https://bib.onroerenderfgoed.be/werken?titel=knokke&per_pagina=10&pagina=1>; rel="first"
X-Content-Type-Options: nosniff
Access-Control-Allow-Origin: *
Set-Cookie: BIGipServerPOOL-AUTO-vioe-informatiecatalogus-prod-std=456599562.20480.0000; path=/; Httponly
X-Permitted-Cross-Domain-Policies: none
X-XSS-Protection: 1; mode=block
[{"publicatietype": {"url": "http://purl.org/ontology/bibo/Document", "naam": "Document", "id": 1}, "titel": "Het landelijke Knokke", "self": "https://bib.onroerenderfgoed.be/werken/552291", "uri": "https://id.erfgoed.net/infocat/werken/552291", "serie": {"naam": "GEEN SERIE", "id": 0}, "auteur": "D'hont A.", "id": 552291, "omschrijving": "Het landelijke Knokke"}, {"jaar": "2011", "publicatietype": {"url": "http://purl.org/ontology/bibo/Document", "naam": "Document", "id": 1}, "titel": "Duin&Water, Knokke", "self": "https://bib.onroerenderfgoed.be/werken/545629", "uri": "https://id.erfgoed.net/infocat/werken/545629", "serie": {"naam": "GEEN SERIE", "id": 0}, "auteur": "Decraemer S.", "id": 545629, "omschrijving": "Duin&Water, Knokke"}, {"jaar": "1976", "publicatietype": {"url": "http://purl.org/ontology/bibo/Document", "naam": "Document", "id": 1}, "titel": "Dagklapper uit Knokke", "self": "https://bib.onroerenderfgoed.be/werken/552280", "uri": "https://id.erfgoed.net/infocat/werken/552280", "serie": {"naam": "GEEN SERIE", "id": 0}, "auteur": "D'Hont A.", "id": 552280, "omschrijving": "Dagklapper uit Knokke"}, {"jaar": "2006", "publicatietype": {"url": "http://purl.org/ontology/bibo/Document", "naam": "Document", "id": 1}, "titel": "Een casino voor de toekomst", "self": "https://bib.onroerenderfgoed.be/werken/545165", "uri": "https://id.erfgoed.net/infocat/werken/545165", "serie": {"naam": "GEEN SERIE", "id": 0}, "id": 545165, "omschrijving": "Een casino voor de toekomst"}, {"jaar": "2004", "publicatietype": {"url": "http://purl.org/ontology/bibo/Document", "naam": "Document", "id": 1}, "titel": "Knokke-Heist natuurlijk", "self": "https://bib.onroerenderfgoed.be/werken/552299", "uri": "https://id.erfgoed.net/infocat/werken/552299", "serie": {"naam": "GEEN SERIE", "id": 0}, "auteur": "Struyf K.", "id": 552299, "omschrijving": "Knokke-Heist natuurlijk"}, {"jaar": "2000", "publicatietype": {"url": "http://purl.org/ontology/bibo/Document", "naam": "Document", "id": 1}, "titel": "Knokke-Heist geboekstaafd", "self": "https://bib.onroerenderfgoed.be/werken/552277", "uri": "https://id.erfgoed.net/infocat/werken/552277", "serie": {"naam": "GEEN SERIE", "id": 0}, "id": 552277, "omschrijving": "Knokke-Heist geboekstaafd"}, {"jaar": "2001", "publicatietype": {"url": "http://purl.org/ontology/bibo/Document", "naam": "Document", "id": 1}, "titel": "Nellens Albertstrand Casino Knokke", "self": "https://bib.onroerenderfgoed.be/werken/552290", "uri": "https://id.erfgoed.net/infocat/werken/552290", "serie": {"naam": "GEEN SERIE", "id": 0}, "auteur": "D'hont A.", "id": 552290, "omschrijving": "Nellens Albertstrand Casino Knokke"}, {"jaar": "2007", "publicatietype": {"url": "http://purl.org/ontology/bibo/Document", "naam": "Document", "id": 1}, "titel": "Archeologisch onderzoek op de hoek van de Kursaalstraat en de Kerkstraat te Knokke-Heist", "self": "https://bib.onroerenderfgoed.be/werken/534021", "uri": "https://id.erfgoed.net/infocat/werken/534021", "serie": {"naam": "GEEN SERIE", "id": 0}, "id": 534021, "omschrijving": "Archeologisch onderzoek op de hoek van de Kursaalstraat en de Kerkstraat te Knokke-Heist"}, {"jaar": "2010", "publicatietype": {"url": "http://purl.org/ontology/bibo/Document", "naam": "Document", "id": 1}, "titel": "Verstild versteend verleden", "self": "https://bib.onroerenderfgoed.be/werken/536850", "uri": "https://id.erfgoed.net/infocat/werken/536850", "serie": {"naam": "GEEN SERIE", "id": 0}, "id": 536850, "omschrijving": "Verstild versteend verleden"}, {"jaar": "1985", "publicatietype": {"url": "http://purl.org/ontology/bibo/Document", "naam": "Document", "id": 1}, "titel": "Het Knokke van toen", "self": "https://bib.onroerenderfgoed.be/werken/13455", "uri": "https://id.erfgoed.net/infocat/werken/13455", "volume": "14", "serie": {"naam": "Het \"Dorp\" van toen", "id": 499957}, "auteur": "LANNOY Danny", "id": 13455, "omschrijving": "Het Knokke van toen"}]
This curl command fetches books and documents (werken) from our library catalog that have knokke in the title. We also ask it to print the headers. Within those headers, we care about the Link header in this case.
Link: <https://bib.onroerenderfgoed.be/werken?titel=knokke&per_pagina=10&pagina=2>; rel="next", <https://bib.onroerenderfgoed.be/werken?titel=knokke&per_pagina=10&pagina=17>; rel="last", <https://bib.onroerenderfgoed.be/werken?titel=knokke&per_pagina=10&pagina=1>; rel="first"
While we immediately get some results back, this is only a partial result set. When we look at the Link header, it tells us there are more pages of results. There’s a next page, a last page and a first page. Since we’re currently at page 1, there’s no previous page. By following the links here, we can either keep on requesting the next page or we can jump straight to the last page. It’s important to note that the client should not assume anything about the links it’s getting back. We might be tempted to calculate that if there are 17 pages and each pages contains at most 10 records, there are between 161 and 170 results. But the server is not obliged to put any of this information in the link or to honor anything about it. The link should treated as just that, a link. It could just as well have been something that is not interpretable. This does reveal one major weakness of using the Link header for pagination purposes. There’s no mechanism to indicate how many results there are or how many links the client will need to follow to reach the end of the result set. AS far as the client is concerned, there’s a next link and a last link. If there’s no next link, we’re at the last page and if the next and last links are identical, we’re at the last-but-one page. But there’s no way to discern when we’re in the middle or how far away we are from the end. On the other hand, our client needs no knowledge whatsoever of the structure inherent in our URL’s. It does not need to understand there’s a page or a pagina parameter or limit and offset parameters or other simiar mechanisms for pagination. This allows us to reuse client code for lots of different webservices. All the client needs to know is that if there’s a next link, it can fetch more results.
So, if we now want to fetch the second page of results, we use this command:
$ curl -G -i -H Accept:application/json https://bib.onroerenderfgoed.be/werken?titel=knokke\&per_pagina=10\&pagina=2
To demonstrate this, we have written a small Python script that queries several of our information systems for information and handles all pagination of results through the Link header. In Python, it’s common to use the requests library for doing calls to REST services. It turns out that we’re even more fortunate, since requests has built in support for the Link header. This makes it dead simple to iterate over all the results. For this little excercise, we’re fetching all the images (Zoeken in de beeldbank), themas (Thema’s, personen en gebeurtenissen), erfgoedobjecten and aanduidingsobjecten (Erfgoedobjecten en aanduidingsobjecten) from the municipality of Knokke-Heist. It takes very little effort to write and drops a mass of URI’s at our feet.
# -*- coding: utf-8 -*-
import requests
# Our endpoints
INVENTARIS = 'https://inventaris.onroerenderfgoed.be'
AFBEELDINGEN = 'https://beeldbank.onroerenderfgoed.be/images'
ERFGOEDOBJECTEN = INVENTARIS + '/erfgoedobjecten'
AANDUIDINGSOBJECTEN = INVENTARIS + '/aanduidingsobjecten'
THEMAS = INVENTARIS + '/themas'
def get_data(url, parameters):
'''
Fetch all data from a url until there are no more `next` urls in the Link
header.
:param str url: The url to fetch from
:param dict parameters: A dict of query string parameters
:rtype: dict
'''
data = []
headers = {'Accept': 'application/json'}
res = requests.get(url, params=parameters, headers=headers)
data.extend(res.json())
while 'next' in res.links:
res = requests.get(res.links['next']['url'], headers=headers)
data.extend(res.json())
return data
# Determine the CRAB ID for the gemeente you want
# https://loc.geopunt.be/v4/Location?q=knokke-heist
MUNICIPALITY_ID = 191
# Fetch all data
afbeeldingen = get_data(AFBEELDINGEN, {'municipality': MUNICIPALITY_ID})
erfgoedobjecten = get_data(ERFGOEDOBJECTEN, {'gemeente': MUNICIPALITY_ID})
aanduidingsobjecten = get_data(AANDUIDINGSOBJECTEN, {'gemeente': MUNICIPALITY_ID})
themas = get_data(THEMAS, {'gemeente': MUNICIPALITY_ID})
# Add everything together
stuff = afbeeldingen + erfgoedobjecten + aanduidingsobjecten + themas
# Print the URIs of all objects involved
uris = [thing['uri'] for thing in stuff]
print(uris)
If we execute this command, we now get a big, long list of URI’s on our screen. It works well, but it’s not that satisfying. A URI might be good for a machine, but we don’t really know what it represents. So, what can we do?
Another interesting technique in the Linked Data world is called JSON-LD (JSON for Linking Data). It allows us to turn plain old JSON into linked data and make our objects more compatible. The basic idea is that we map our JSON dataset to actual RDF objects by defining a context. In this context, we map keys in our JSON to RDF properties. As an example, here’s a simple context that can be used on our image database endpoint:
{
"dct": "http://purl.org/dc/terms/",
"title": "dct:title",
"description": "dct:description",
"uri": "@id"
}
This context defines a namespace shorthand, dct, and maps it to the Dublin Core Terms namespace http://purl.org/dc/terms/. It allows us to write dct:title as shorthand for http://purl.org/dc/terms/title. We have also defined uri to be an alias for @id. @id in JSON-ld indicates the unique identifier for a certain record or resource. Applying this context to an image from our image database gives us the following JSON data:
{
"@id": "https://id.erfgoed.net/afbeeldingen/392715",
"http://purl.org/dc/terms/title": "Villa Cools met kinesistenpraktijk",
"http://purl.org/dc/terms/description": "",
}
This tells us there’s a resource with the id https://id.erfgoed.net/afbeeldingen/392715 that can be described with a title from the Dublin Core Terms vocabulary. Quite common in JSON-LD would be to assign a type to the resource we’re talking about. We might know this record comes from the image database, but it’s currently not reflected in our data. Once we add data from the inventory we might not be able to tell the difference anymore. In JSON-LD we can add a @type key to make this explicit. We set it to the URI of a class from an RDF vocabulary like this:
{
"@id": "https://id.erfgoed.net/afbeeldingen/392715",
"@type": "http://purl.org/dc/dcmitype/Image",
"http://purl.org/dc/terms/title": "Villa Cools met kinesistenpraktijk",
"http://purl.org/dc/terms/description": "",
}
We’ll use JSON-LD to enhance our script. We want to homogenise our dataset of all information about Knokke-Heist, so we are left with only records having the same structure. We’ve made two JSON-LD contexts, one for the image database and a second one for the three datasets originating from our inventory. Because the datamodels for these three datasets are extremely similar datamodels. The datasets don’t normally include the type of resource they contain but we do want to be able to tell the difference in our final dataset, so we’ll add those here as well. Our inventory system has a field called locatie_samenvatting that provides a summary of the geographic location of an object. While the summary algorithm can be fairly complicated (there’s things that are located in more than one street in more than one municiaplity or even more than one province), it’s very useful for a human to quickly see where something can be found. There’s no such field in the image database and we’ve created a quick replacement summary for this dataset (luckily the location of images is much simpler than that of heritage objects).
Our script fetches all data like before, but we now add the type of record we’re fetching and a simple location summary for images. Then we expand all of them into JSON-LD using the pyLD Python library. This leaves us with a dataset that is valid JSON-LD, but a bit unwieldy. Every key in the dataset is now identical, but it is a URI, which is not very practical. So, we’ll simplify things for ourselves, again using JSON-LD. We’ve defined a third context based on the Dublin Core Terms vocabulary we’ll use to compact our existing JSON-LD. Finally we print them to the screen.
# -*- coding: utf-8 -*-
import requests
from pyld import jsonld
import json
# Our endpoints
INVENTARIS = 'https://inventaris.onroerenderfgoed.be'
AFBEELDINGEN = 'https://beeldbank.onroerenderfgoed.be/images'
ERFGOEDOBJECTEN = INVENTARIS + '/erfgoedobjecten'
AANDUIDINGSOBJECTEN = INVENTARIS + '/aanduidingsobjecten'
THEMAS = INVENTARIS + '/themas'
def get_data(url, parameters):
'''
Fetch all data from a url until there are no more `next` urls in the Link
header.
:param str url: The url to fetch from
:param dict parameters: A dict of query string parameters
:rtype: dict
'''
data = []
headers = {'Accept': 'application/json'}
res = requests.get(url, params=parameters, headers=headers)
data.extend(res.json())
while 'next' in res.links:
res = requests.get(res.links['next']['url'], headers=headers)
data.extend(res.json())
return data
def add_type(collection, rtype):
"""
Add the resource type to a resource
:param list collection: Collection of resources to add a type to
:param str rtype: The type of all resources in this collection
:rtype: list
"""
for c in collection:
c.update({'@type': rtype})
def add_locatie_samenvatting(afbeeldingen):
"""
Summarize the location of an image
:param list afbeeldingen: Collection of afbeeldingen to summarize
:rtype: list
"""
for a in afbeeldingen:
s = ''
hnr = a.get('location', {}).get('housenumber', {}).get('name')
straat = a.get('location', {}).get('street', {}).get('name')
gemeente = a.get('location', {}).get('municipality', {}).get('name')
prov = a.get('location', {}).get('province', {}).get('name')
if straat and hnr:
s = '{} {} ({})'.format(straat, hnr, gemeente)
elif straat:
s = '{} ({})'.format(straat, gemeente)
else:
s = '{} ({})'.format(gemeente, prov)
a.update({'locatie_samenvatting': s})
# Determine the CRAB ID for the gemeente you want
# https://loc.geopunt.be/v4/Location?q=knokke-heist
MUNICIPALITY_ID = 191
# Fetch all data
afbeeldingen = get_data(AFBEELDINGEN, {'municipality': MUNICIPALITY_ID})
erfgoedobjecten = get_data(ERFGOEDOBJECTEN, {'gemeente': MUNICIPALITY_ID})
aanduidingsobjecten = get_data(AANDUIDINGSOBJECTEN, {'gemeente': MUNICIPALITY_ID})
themas = get_data(THEMAS, {'gemeente': MUNICIPALITY_ID})
# Add everything together and transform to linked data
inventaris_context = {
"dct": "http://purl.org/dc/terms/",
"naam": "dct:title",
"korte_beschrijving": "dct:description",
"locatie_samenvatting": "dct:spatial",
"uri": "@id",
"Thema": "https://id.erfgoed.net/vocab/ontology#Thema",
"Erfgoedobject": "https://id.erfgoed.net/vocab/ontology#Erfgoedobject",
"Aanduidingsobject": "https://id.erfgoed.net/vocab/ontology#Aanduidingsobject"
}
beeldbank_context = {
"dct": "http://purl.org/dc/terms/",
"title": "dct:title",
"description": "dct:description",
"locatie_samenvatting": "dct:spatial",
"uri": "@id",
"Afbeelding": "https://purl.org/dc/dcmiType/Image"
}
# Add types to all datasets and location summary to images
add_type(erfgoedobjecten, "Erfgoedobject")
erfgoedobjecten = jsonld.expand(erfgoedobjecten, {'expandContext':inventaris_context})
add_type(aanduidingsobjecten, "Aanduidingsobject")
aanduidingsobjecten = jsonld.expand(aanduidingsobjecten, {'expandContext':inventaris_context})
add_type(themas, "Thema")
themas = jsonld.expand(themas, {'expandContext':inventaris_context})
add_type(afbeeldingen, "Afbeelding")
add_locatie_samenvatting(afbeeldingen)
afbeeldingen = jsonld.expand(afbeeldingen, {'expandContext':beeldbank_context})
# Add all datasets together
stuff = erfgoedobjecten + aanduidingsobjecten + themas + afbeeldingen
# Compact all data to simplify the keys we're working with
dct_context = {
"dct": "http://purl.org/dc/terms/",
"title": "dct:title",
"description": "dct:description",
"spatial": "dct:spatial",
"uri": "@id",
"type": "@type",
"Thema": "https://id.erfgoed.net/vocab/ontology#Thema",
"Erfgoedobject": "https://id.erfgoed.net/vocab/ontology#Erfgoedobject",
"Aanduidingsobject": "https://id.erfgoed.net/vocab/ontology#Aanduidingsobject",
"Afbeelding": "https://purl.org/dc/dcmiType/Image"
}
compactstuff = jsonld.compact(stuff, dct_context)
# Print all records to the screen
for s in compactstuff['@graph']:
h = '{}'.format(s['title'])
print(h)
print(len(h)*'=')
print('Type: {}'.format(s['type']))
print('URI: {}'.format(s['uri']))
print('Location: {}'.format(s['spatial']))
if 'description' in s and s['description']:
print(s['description'])
print()
To run this script, don’t forget to install the requests and pyLD
libraries. If you now run this script, you should see a whole list of
information printed to the screen. It make take a while though, especially for
the large municipalities like Gent or Antwerpen. As a little encore, we’ve
added another version of this script that doesn’t print to the screen, but writes
our new dataset to a csv for further processing. Have a look at the
final set of data for Knokke-Heist this
script produces or modify the script to generate your own dataset.
# -*- coding: utf-8 -*-
import requests
from pyld import jsonld
import json
import csv
# Our endpoints
INVENTARIS = 'https://inventaris.onroerenderfgoed.be'
AFBEELDINGEN = 'https://beeldbank.onroerenderfgoed.be/images'
ERFGOEDOBJECTEN = INVENTARIS + '/erfgoedobjecten'
AANDUIDINGSOBJECTEN = INVENTARIS + '/aanduidingsobjecten'
THEMAS = INVENTARIS + '/themas'
def get_data(url, parameters):
'''
Fetch all data from a url until there are no more `next` urls in the Link
header.
:param str url: The url to fetch from
:param dict parameters: A dict of query string parameters
:rtype: dict
'''
data = []
headers = {'Accept': 'application/json'}
res = requests.get(url, params=parameters, headers=headers)
data.extend(res.json())
while 'next' in res.links:
res = requests.get(res.links['next']['url'], headers=headers)
data.extend(res.json())
return data
def add_type(collection, rtype):
"""
Add the resource type to a resource
:param list collection: Collection of resources to add a type to
:param str rtype: The type of all resources in this collection
:rtype: list
"""
for c in collection:
c.update({'@type': rtype})
def add_locatie_samenvatting(afbeeldingen):
"""
Summarize the location of an image
:param list afbeeldingen: Collection of afbeeldingen to summarize
:rtype: list
"""
for a in afbeeldingen:
s = ''
hnr = a.get('location', {}).get('housenumber', {}).get('name')
straat = a.get('location', {}).get('street', {}).get('name')
gemeente = a.get('location', {}).get('municipality', {}).get('name')
prov = a.get('location', {}).get('province', {}).get('name')
if straat and hnr:
s = '{} {} ({})'.format(straat, hnr, gemeente)
elif straat:
s = '{} ({})'.format(straat, gemeente)
else:
s = '{} ({})'.format(gemeente, prov)
a.update({'locatie_samenvatting': s})
# Determine the CRAB ID for the gemeente you want
# https://loc.geopunt.be/v4/Location?q=knokke-heist
MUNICIPALITY_ID = 191
# Fetch all data
afbeeldingen = get_data(AFBEELDINGEN, {'municipality': MUNICIPALITY_ID})
erfgoedobjecten = get_data(ERFGOEDOBJECTEN, {'gemeente': MUNICIPALITY_ID})
aanduidingsobjecten = get_data(AANDUIDINGSOBJECTEN, {'gemeente': MUNICIPALITY_ID})
themas = get_data(THEMAS, {'gemeente': MUNICIPALITY_ID})
# Add everything together and transform to linked data
inventaris_context = {
"dct": "http://purl.org/dc/terms/",
"naam": "dct:title",
"korte_beschrijving": "dct:description",
"locatie_samenvatting": "dct:spatial",
"uri": "@id",
"Thema": "https://id.erfgoed.net/vocab/ontology#Thema",
"Erfgoedobject": "https://id.erfgoed.net/vocab/ontology#Erfgoedobject",
"Aanduidingsobject": "https://id.erfgoed.net/vocab/ontology#Aanduidingsobject"
}
beeldbank_context = {
"dct": "http://purl.org/dc/terms/",
"title": "dct:title",
"description": "dct:description",
"locatie_samenvatting": "dct:spatial",
"uri": "@id",
"Afbeelding": "https://purl.org/dc/dcmiType/Image"
}
# Add types to all datasets and location summary to images
add_type(erfgoedobjecten, "Erfgoedobject")
erfgoedobjecten = jsonld.expand(erfgoedobjecten, {'expandContext':inventaris_context})
add_type(aanduidingsobjecten, "Aanduidingsobject")
aanduidingsobjecten = jsonld.expand(aanduidingsobjecten, {'expandContext':inventaris_context})
add_type(themas, "Thema")
themas = jsonld.expand(themas, {'expandContext':inventaris_context})
add_type(afbeeldingen, "Afbeelding")
add_locatie_samenvatting(afbeeldingen)
afbeeldingen = jsonld.expand(afbeeldingen, {'expandContext':beeldbank_context})
# Add all datasets together
stuff = erfgoedobjecten + aanduidingsobjecten + themas + afbeeldingen
# Compact all data to simplify the keys we're working with
dct_context = {
"dct": "http://purl.org/dc/terms/",
"title": "dct:title",
"description": "dct:description",
"spatial": "dct:spatial",
"uri": "@id",
"type": "@type",
"Thema": "https://id.erfgoed.net/vocab/ontology#Thema",
"Erfgoedobject": "https://id.erfgoed.net/vocab/ontology#Erfgoedobject",
"Aanduidingsobject": "https://id.erfgoed.net/vocab/ontology#Aanduidingsobject",
"Afbeelding": "https://purl.org/dc/dcmiType/Image"
}
compactstuff = jsonld.compact(stuff, dct_context)
# Write all data to a csv
with open('heritage.csv', 'w', newline='') as csvfile:
fieldnames = ['uri', 'type', 'title', 'description', 'spatial']
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader()
for s in compactstuff['@graph']:
writer.writerow(s)
If you’re interested in things such as Hypermedia API’s and Linked Data, be sure to check out https://data.vlaanderen.be/ as it contains much more information about these topics and points you to all the relevant documents and standards. There’s a whole community of people knowledgeable about Linked Data out there who are more than willing to answer your questions, you’ll find quite a few of them on Twitter. As always, feel free to contact us with further questions.