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LeMyst / WikibaseIntegrator

A Python module to manipulate data on a Wikibase instance (like Wikidata) through the MediaWiki Wikibase API and the Wikibase SPARQL endpoint.
MIT License
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mediawiki sparql wikibase wikidata wikidata-api wikidata-toolkit

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Wikibase Integrator

PyPi Python pytest Python Code Quality and Lint CodeQL Pyversions Read the Docs

Wikibase Integrator is a python package whose purpose is to manipulate data present on a Wikibase instance (like Wikidata).

Breaking changes in v0.12

A complete rewrite of the WikibaseIntegrator core has been done in v0.12 which has led to some important changes.

It offers a new object-oriented approach, better code readability and support for Property, Lexeme and MediaInfo entities (in addition to Item).

If you want to stay on v0.11.x, you can put this line in your requirements.txt:

wikibaseintegrator~=0.11.3

WikibaseIntegrator / WikidataIntegrator

WikibaseIntegrator (wbi) is a fork of WikidataIntegrator (wdi) whose purpose is to focus on an improved compatibility with Wikibase and adding missing functionalities. The main differences between these two libraries are :

But WikibaseIntegrator lack the "fastrun" functionality implemented in WikidataIntegrator.

Documentation

A (basic) documentation generated from the python source code is available on the Read the Docs website.

Jupyter notebooks

You can find some sample code (adding an entity, a lexeme, etc.) in the Jupyter notebook directory of the repository.

Common use cases

Read an existing entity

Get the entity with the QID Q582 from the instance.

From import_entity.ipynb

entity = wbi.item.get('Q582')

Start a new entity

Start a new local entity.

From item_create_new.ipynb

entity = wbi.item.new()

Write an entity to instance

Write a local entity to the instance.

From import_entity.ipynb

entity.write()

Add labels

Add an English and a French label to the local entity.

From item_create_new.ipynb

entity.labels.set('en', 'New item')
entity.labels.set('fr', 'Nouvel élément')

Get label value

Get the english label value of the local entity.

From item_get.ipynb

entity.labels.get('en').value

Add aliases

Add an English and a French alias to the local entity.

From item_create_new.ipynb

entity.aliases.set('en', 'Item')
entity.aliases.set('fr', 'Élément')

Add descriptions

Add an English and a French description to the local entity.

From item_create_new.ipynb

entity.descriptions.set('en', 'A freshly created element')
entity.descriptions.set('fr', 'Un élément fraichement créé')

Add a simple claim

Add a Time claim with the property P74 and the current time to the local entity.

From item_create_new.ipynb

claim_time = datatypes.Time(prop_nr='P74', time='now')

entity.claims.add(claim_time)

Get claim value

Get the value of the first claim with the property P2048 of the local entity.

From item_get.ipynb

entity.claims.get('P2048')[0].mainsnak.datavalue['value']['amount']

Manipulate claim, add a qualifier

From item_create_new.ipynb

qualifiers = Qualifiers()
qualifiers.add(datatypes.String(prop_nr='P828', value='Item qualifier'))

claim_string = datatypes.String(prop_nr='P31533', value='A String property', qualifiers=qualifiers)
entity.claims.add(claim_string)

Manipulate claim, add references

From item_create_new.ipynb

references = References()
reference1 = Reference()
reference1.add(datatypes.String(prop_nr='P828', value='Item string reference'))

reference2 = Reference()
reference2.add(datatypes.String(prop_nr='P828', value='Another item string reference'))

references.add(reference1)
references.add(reference2)

new_claim_string = datatypes.String(prop_nr='P31533', value='A String property', references=references)
entity.claims.add(claim_string)

Remove a specific claim

Remove all claims with the property P31533 and the value Q123 from the local entity.

claims = entity.claims.get('P31533')
for claim in claims:
    if claim.mainsnak.datavalue['value']['id'] == 'Q123':
        claim.remove()

Get lemma on lexeme

Get all French lemmas of the lexeme.

lexeme.lemmas.get(language='fr')

Set lemma on lexeme

Add a French lemma with the value 'réponse' to the lexeme.

From lexeme_update.ipynb

lexeme.lemmas.set(language='fr', value='réponse')

Add gloss to a sense on lexeme

From lexeme_write.ipynb

sense = Sense()
sense.glosses.set(language='en', value='English gloss')
sense.glosses.set(language='fr', value='French gloss')
claim = datatypes.String(prop_nr='P828', value="Create a string claim for sense")
sense.claims.add(claim)
lexeme.senses.add(sense)

Add form to a lexeme

From lexeme_write.ipynb

form = Form()
form.representations.set(language='en', value='English form representation')
form.representations.set(language='fr', value='French form representation')
claim = datatypes.String(prop_nr='P828', value="Create a string claim for form")
form.claims.add(claim)
lexeme.forms.add(form)

Other projects

Here is a list of different projects that use the library:

Installation

The easiest way to install WikibaseIntegrator is to use the pip package manager. WikibaseIntegrator supports Python 3.9 and above. If Python 2 is installed, pip will lead to an error indicating missing dependencies.

python -m pip install wikibaseintegrator

You can also clone the repo and run it with administrator rights or install it in a virtualenv.

git clone https://github.com/LeMyst/WikibaseIntegrator.git

cd WikibaseIntegrator

python -m pip install --upgrade pip setuptools

python -m pip install .

You can also use Poetry:

python -m pip install --upgrade poetry

python -m poetry install

To check that the installation is correct, launch a Python console and run the following code (which will retrieve the Wikidata element for Human):

from wikibaseintegrator import WikibaseIntegrator

wbi = WikibaseIntegrator()
my_first_wikidata_item = wbi.item.get(entity_id='Q5')

# to check successful installation and retrieval of the data, you can print the json representation of the item
print(my_first_wikidata_item.get_json())

Using a Wikibase instance

WikibaseIntegrator uses Wikidata as default endpoint. To use another instance of Wikibase instead, you can override the wbi_config module.

An example for a Wikibase instance installed with wikibase-docker, add this to the top of your script:

from wikibaseintegrator.wbi_config import config as wbi_config

wbi_config['MEDIAWIKI_API_URL'] = 'http://localhost/api.php'
wbi_config['SPARQL_ENDPOINT_URL'] = 'http://localhost:8834/proxy/wdqs/bigdata/namespace/wdq/sparql'
wbi_config['WIKIBASE_URL'] = 'http://wikibase.svc'

You can find more default settings in the file wbi_config.py

Wikimedia Foundation User-Agent policy

If you interact with a Wikibase instance hosted by the Wikimedia Foundation (like Wikidata, Wikimedia Commons, etc.), it's highly advised to follow the User-Agent policy that you can find on the page User-Agent policy of the Wikimedia Meta-Wiki.

You can set a complementary User-Agent by modifying the variable wbi_config['USER_AGENT'] in wbi_config.

For example, with your library name and contact information:

from wikibaseintegrator.wbi_config import config as wbi_config

wbi_config['USER_AGENT'] = 'MyWikibaseBot/1.0 (https://www.wikidata.org/wiki/User:MyUsername)'

The Core Parts

WikibaseIntegrator supports two modes in which it can be used, a normal mode, updating each item at a time, and a fast run mode, which preloads some data locally and then just updates items if the new data provided differs from Wikidata. The latter mode allows for great speedups when tens of thousands of Wikidata elements need to be checked for updates, but only a small number will eventually be updated, a situation typically encountered when synchronising Wikidata with an external resource.

Entity manipulation

WikibaseIntegrator supports the manipulation of Item, Property, Lexeme and MediaInfo entities through these classes:

Features:

wbi_login

wbi_login provides the login functionality and also stores the cookies and edit tokens required (For security reasons, every MediaWiki edit requires an edit token). There is multiple methods to login:

There is more parameters available. If you want to authenticate on another instance than Wikidata, you can set the mediawiki_api_url, mediawiki_rest_url or mediawiki_index_url. Read the documentation for more information.

Login using OAuth1 or OAuth2

OAuth is the authentication method recommended by the MediaWiki developers. It can be used to authenticate a bot or to use WBI as a backend for an application.

As a bot

If you want to use WBI with a bot account, you should use OAuth as an Owner-only consumer. This allows to use the authentication without the "continue oauth" step.

The first step is to request a new OAuth consumer on your MediaWiki instance on the page "Special:OAuthConsumerRegistration", the "Owner-only" (or "This consumer is for use only by ...") has to be checked and the correct version of the OAuth protocol must be set (OAuth 2.0). You will get a consumer token and consumer secret (and an access token and access secret if you chose OAuth 1.0a). For a Wikimedia instance (like Wikidata), you need to use the Meta-Wiki website.

Example if you use OAuth 2.0:

from wikibaseintegrator import wbi_login

login_instance = wbi_login.OAuth2(consumer_token='<your_client_app_key>', consumer_secret='<your_client_app_secret>')

Example if you use OAuth 1.0a:

from wikibaseintegrator import wbi_login

login_instance = wbi_login.OAuth1(consumer_token='<your_consumer_key>', consumer_secret='<your_consumer_secret>',
                                  access_token='<your_access_token>', access_secret='<your_access_secret>')

To impersonate a user (OAuth 1.0a)

If WBI is to be used as a backend for a web application, the script must use OAuth for authentication, WBI supports this, you just need to specify consumer key and consumer secret when instantiating wbi_login.Login. Unlike login by username and password, OAuth is a 2-step process, as manual confirmation of the user for the OAuth login is required. This means that the wbi_login.OAuth1.continue_oauth() method must be called after creating the wbi_login.Login instance.

Example:

from wikibaseintegrator import wbi_login

login_instance = wbi_login.OAuth1(consumer_token='<your_consumer_key>', consumer_secret='<your_consumer_secret>')
login_instance.continue_oauth(oauth_callback_data='<the_callback_url_returned>')

The wbi_login.OAuth1.continue_oauth() method will either ask the user for a callback URL (normal bot execution) or take a parameter. Thus, in the case where WBI is used as a backend for a web application for example, the callback will provide the authentication information directly to the backend and thus no copy and paste of the callback URL is needed.

Login with a bot password

It's a good practice to use Bot password instead of simple username and password, this allows limiting the permissions given to the bot.

from wikibaseintegrator import wbi_login

login_instance = wbi_login.Login(user='<bot user name>', password='<bot password>')

Login with a username and a password

If you want to log in with your user account, you can use the "clientlogin" authentication method. This method is not recommended.

from wikibaseintegrator import wbi_login

login_instance = wbi_login.Clientlogin(user='<user name>', password='<password>')

Wikibase Data Types

Currently, Wikibase supports 17 different data types. The data types are represented as their own classes in wikibaseintegrator.datatypes. Each datatype has its own peculiarities, which means that some of them require special parameters (e.g. Globe Coordinates). They are available under the namespace wikibase.datatypes.

The data types currently implemented:

Two additional data types are also implemented but require the installation of the MediaWiki extension to work properly:

For details of how to create values (=instances) with these data types, please (for now) consult the docstrings in the source code or the documentation website. Of note, these data type instances hold the values and, if specified, data type instances for references and qualifiers.

Structured Data on Commons

WikibaseIntegrator supports SDC (Structured Data on Commons) to update a media file hosted on Wikimedia Commons.

Retrieve data

from wikibaseintegrator import WikibaseIntegrator

wbi = WikibaseIntegrator()
media = wbi.mediainfo.get('M16431477')

# Retrieve the first "depicts" (P180) claim
print(media.claims.get('P180')[0].mainsnak.datavalue['value']['id'])

Write data

from wikibaseintegrator import WikibaseIntegrator
from wikibaseintegrator.datatypes import Item

wbi = WikibaseIntegrator()
media = wbi.mediainfo.get('M16431477')

# Add the "depicts" (P180) claim
media.claims.add(Item(prop_nr='P180', value='Q3146211'))

media.write()

More than Wikibase

WikibaseIntegrator natively supports some extensions:

Helper Methods

Use MediaWiki API

The method wbi_helpers.mediawiki_api_call_helper() allows you to execute MediaWiki API POST call. It takes a mandatory data array (data) and multiple optionals parameters like a login object of type wbi_login.Login, a mediawiki_api_url string if the MediaWiki is not Wikidata, a user_agent string to set a custom HTTP User Agent header, and an allow_anonymous boolean to force authentication.

Example:

Retrieve last 10 revisions from Wikidata element Q2 (Earth):

from wikibaseintegrator import wbi_helpers

data = {
    'action': 'query',
    'prop': 'revisions',
    'titles': 'Q2',
    'rvlimit': 10
}

print(wbi_helpers.mediawiki_api_call_helper(data=data, allow_anonymous=True))

Execute SPARQL queries

The method wbi_helpers.execute_sparql_query() allows you to execute SPARQL queries without a hassle. It takes the actual query string (query), optional prefixes (prefix) if you do not want to use the standard prefixes of Wikidata, the actual endpoint URL (endpoint), and you can also specify a user agent for the http header sent to the SPARQL server ( user_agent). The latter is very useful to let the operators of the endpoint know who you are, especially if you execute many queries on the endpoint. This allows the operators of the endpoint to contact you (e.g. specify an email address, or the URL to your bot code repository.)

Wikibase search entities

The method wbi_helpers.search_entities() allows for string search in a Wikibase instance. This means that labels, descriptions and aliases can be searched for a string of interest. The method takes five arguments: The actual search string (search_string), an optional server (mediawiki_api_url, in case the Wikibase instance used is not Wikidata), an optional user_agent, an optional max_results (default 500), an optional language (default 'en'), and an option dict_id_label to return a dict of item id and label as a result.

Merge Wikibase items

Sometimes, Wikibase items need to be merged. An API call exists for that, and wbi_core implements a method accordingly. wbi_helpers.merge_items() takes five arguments:

Examples (in "normal" mode)

In order to create a minimal bot based on wbi_core, two things are required:

An optional Login object can be used to be authenticated on the Wikibase instance.

Create a new Item

from wikibaseintegrator import wbi_login, WikibaseIntegrator
from wikibaseintegrator.datatypes import ExternalID
from wikibaseintegrator.wbi_config import config as wbi_config

wbi_config['USER_AGENT'] = 'MyWikibaseBot/1.0 (https://www.wikidata.org/wiki/User:MyUsername)'

# login object
login_instance = wbi_login.OAuth2(consumer_token='<consumer_token>', consumer_secret='<consumer_secret>')

wbi = WikibaseIntegrator(login=login_instance)

# data type object, e.g. for a NCBI gene entrez ID
entrez_gene_id = ExternalID(value='<some_entrez_id>', prop_nr='P351')

# data goes into a list, because many data objects can be provided to
data = [entrez_gene_id]

# Create a new item
item = wbi.item.new()

# Set an english label
item.labels.set(language='en', value='Newly created item')

# Set a French description
item.descriptions.set(language='fr', value='Une description un peu longue')

item.claims.add(data)
item.write()

Modify an existing item

from wikibaseintegrator import wbi_login, WikibaseIntegrator
from wikibaseintegrator.datatypes import ExternalID
from wikibaseintegrator.wbi_enums import ActionIfExists
from wikibaseintegrator.wbi_config import config as wbi_config

wbi_config['USER_AGENT'] = 'MyWikibaseBot/1.0 (https://www.wikidata.org/wiki/User:MyUsername)'

# login object
login_instance = wbi_login.OAuth2(consumer_token='<consumer_token>', consumer_secret='<consumer_secret>')

wbi = WikibaseIntegrator(login=login_instance)

# data type object, e.g. for a NCBI gene entrez ID
entrez_gene_id = ExternalID(value='<some_entrez_id>', prop_nr='P351')

# data goes into a list, because many data objects can be provided to
data = [entrez_gene_id]

# Search and then edit an Item
item = wbi.item.get(entity_id='Q141806')

# Set an english label but don't modify it if there is already an entry
item.labels.set(language='en', value='An updated item', action_if_exists=ActionIfExists.KEEP)

# Set a French description and replace the existing one
item.descriptions.set(language='fr', value='Une description un peu longue', action_if_exists=ActionIfExists.REPLACE_ALL)

item.claims.add(data)
item.write()

A bot for Mass Import

An enhanced example of the previous bot just puts two of the three things into a 'for loop' and so allows mass creation, or modification of items.

from wikibaseintegrator import WikibaseIntegrator, wbi_login
from wikibaseintegrator.datatypes import ExternalID, Item, String, Time
from wikibaseintegrator.wbi_config import config as wbi_config
from wikibaseintegrator.wbi_enums import WikibaseTimePrecision

wbi_config['USER_AGENT'] = 'MyWikibaseBot/1.0 (https://www.wikidata.org/wiki/User:MyUsername)'

# login object
login_instance = wbi_login.OAuth2(consumer_token='<consumer_token>', consumer_secret='<consumer_secret>')

# We have raw data, which should be written to Wikidata, namely two human NCBI entrez gene IDs mapped to two Ensembl Gene IDs
raw_data = {
    '50943': 'ENST00000376197',
    '1029': 'ENST00000498124'
}

wbi = WikibaseIntegrator(login=login_instance)

for entrez_id, ensembl in raw_data.items():
    # add some references
    references = [
        [
            Item(value='Q20641742', prop_nr='P248'),
            Time(time='+2020-02-08T00:00:00Z', prop_nr='P813', precision=WikibaseTimePrecision.DAY),
            ExternalID(value='1017', prop_nr='P351')
        ]
    ]

    # data type object
    entrez_gene_id = String(value=entrez_id, prop_nr='P351', references=references)
    ensembl_transcript_id = String(value=ensembl, prop_nr='P704', references=references)

    # data goes into a list, because many data objects can be provided to
    data = [entrez_gene_id, ensembl_transcript_id]

    # Search for and then edit/create new item
    item = wbi.item.new()
    item.claims.add(data)
    item.write()

Examples (in "fast run" mode)

In order to use the fast run mode, you need to know the property/value combination which determines the data corpus you would like to operate on. E.g. for operating on human genes, you need to know that P351 is the NCBI Entrez Gene ID and you also need to know that you are dealing with humans, best represented by the found in taxon property (P703) with the value Q15978631 for Homo sapiens.

IMPORTANT: In order for the fast run mode to work, the data you provide in the constructor must contain at least one unique value/id only present on one Wikidata element, e.g. an NCBI entrez gene ID, Uniprot ID, etc. Usually, these would be the same unique core properties used for defining domains in wbi_core, e.g. for genes, proteins, drugs or your custom domains.

Below, the normal mode run example from above, slightly modified, to meet the requirements for the fast run mode. To enable it, ItemEngine requires two parameters, fast_run=True/False and fast_run_base_filter which is a dictionary holding the properties to filter for as keys, and the item QIDs as dict values. If the value is not a QID but a literal, just provide an empty string. For the above example, the dictionary looks like this:

from wikibaseintegrator.datatypes import ExternalID, Item

fast_run_base_filter = [ExternalID(prop_nr='P351'), Item(prop_nr='P703', value='Q15978631')]

The full example:

from wikibaseintegrator import WikibaseIntegrator, wbi_login
from wikibaseintegrator.datatypes import ExternalID, Item, String, Time
from wikibaseintegrator.wbi_enums import WikibaseTimePrecision

# login object
login = wbi_login.OAuth2(consumer_token='<consumer_token>', consumer_secret='<consumer_secret>')

fast_run_base_filter = [ExternalID(prop_nr='P351'), Item(prop_nr='P703', value='Q15978631')]
fast_run = True

# We have raw data, which should be written to Wikidata, namely two human NCBI entrez gene IDs mapped to two Ensembl Gene IDs
# You can iterate over any data source as long as you can map the values to Wikidata properties.
raw_data = {
    '50943': 'ENST00000376197',
    '1029': 'ENST00000498124'
}

for entrez_id, ensembl in raw_data.items():
    # add some references
    references = [
        [
            Item(value='Q20641742', prop_nr='P248')
        ],
        [
            Time(time='+2020-02-08T00:00:00Z', prop_nr='P813', precision=WikibaseTimePrecision.DAY),
            ExternalID(value='1017', prop_nr='P351')
        ]
    ]

    # data type object
    entrez_gene_id = String(value=entrez_id, prop_nr='P351', references=references)
    ensembl_transcript_id = String(value=ensembl, prop_nr='P704', references=references)

    # data goes into a list, because many data objects can be provided to
    data = [entrez_gene_id, ensembl_transcript_id]

    # Search for and then edit/create new item
    wb_item = WikibaseIntegrator(login=login).item.new()
    wb_item.add_claims(claims=data)
    wb_item.init_fastrun(base_filter=fast_run_base_filter)
    wb_item.write()

Note: Fastrun mode checks for equality of property/value pairs, qualifiers (not including qualifier attributes), labels, aliases and description, but it ignores references by default! References can be checked in fast run mode by setting use_refs to True.

Debugging

You can enable debugging by adding this piece of code to the top of your project:

import logging

logging.basicConfig(level=logging.DEBUG)