Wikibase Integrator

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

Breaking changes in v0.13

v0.13 contains changes that are not backward compatible with v0.12:

• Python 3.11 or newer is required. Python 3.9 and 3.10 are no longer supported.
• The fast run mode has been rewritten (see Examples (in "fast run" mode)). Code using wbi_fastrun directly needs to be updated:
  • FastRunContainer.get_items() is renamed get_entities().
  • FastRunContainer() takes base_filter as its first, mandatory argument and base_data_type is now optional. use_refs is renamed use_references, and use_qualifiers (default True), use_rank and cache are added.
  • FastRunContainer.write_required() drops cqid and gains entity_filter and property_filter.
  • BaseEntity.write_required() now requires base_filter.
  • Data types compared through SPARQL implement from_sparql_value(), which replaces parse_sparql_value() for the fast run mode.
• Form and Sense compare and hash by content (representations or glosses, grammatical features, claims) instead of by identity, and ignore their id. Two distinct objects holding the same content are now equal, and a Form/Sense modified after being added to a set or used as a dict key can no longer be found there.
• Claims.add() appends by default: its action_if_exists default is now ActionIfExists.APPEND_OR_REPLACE instead of REPLACE_ALL, like BaseEntity.add_claims(). Adding a claim no longer removes the existing claims with the same property. Pass action_if_exists=ActionIfExists.REPLACE_ALL to keep the previous behavior. Re-adding an existing value now keeps its rank and references, unless the new claim sets them.
• HTTP requests have a default timeout of 5 seconds to connect and 300 seconds to read. Change it with wbi_config['TIMEOUT'], or set it to None to wait forever as before.
• Fewer retries by default: max_retries defaults to 100 instead of 1000 in mediawiki_api_call_helper().
• execute_sparql_query() retries through wbi_backoff: its max_retries parameter is ignored (with a DeprecationWarning). Failed queries are retried up to wbi_config['BACKOFF_MAX_TRIES'] times (5 by default), with an exponential wait capped by wbi_config['BACKOFF_MAX_VALUE'], instead of up to 1000 times every 60 seconds. Once the retries are exhausted, the last error is raised (e.g. requests.HTTPError) instead of a bare Exception. Authentication errors (HTTP 401/403) are not retried.
• Anonymous edits that are not explicitly allowed raise AnonymousEditNotAllowedError instead of a bare Exception.
• The enums mirroring API values compare equal to them: WikibaseDatatype, WikibaseRank and WikibaseSnakType are StrEnum, and WikibaseTimePrecision is an IntEnum. A member is now equal to its API value (WikibaseRank.PREFERRED == 'preferred', WikibaseTimePrecision.DAY == 11, both were False), and str() gives that value ('preferred' instead of 'WikibaseRank.PREFERRED'). The JSON sent to the API is unchanged.
• Empty values are rejected before the write: a claim, qualifier or reference created with None or an empty string as value raises EmptyValueError (a ValueError) listing every empty value, instead of being sent and rejected by the API with an MWApiError. Use the WikibaseSnakType.NO_VALUE or WikibaseSnakType.UNKNOWN_VALUE snak type to state that there is no value or an unknown value.
• mwoauth is no longer a dependency: OAuth1 is implemented with oauthlib directly. Install mwoauth yourself if your own code imports it.
• wbi_helpers.default_session and wbi_helpers.helpers_session are deprecated: requests made without a login now use a private session per thread. The old names still work but emit a DeprecationWarning.

If you want to stay on v0.12.x, you can put this line in your requirements.txt:

wikibaseintegrator~=0.12.15

• WikibaseIntegrator / WikidataIntegrator
• Documentation
  • Jupyter notebooks
    • Common use cases
      • Read an existing entity
      • Start a new entity
      • Write an entity to instance
      • Add labels
      • Get label value
      • Add aliases
      • Add descriptions
      • Add a simple claim
      • Get claim value
      • Manipulate claim, add a qualifier
      • Manipulate claim, add references
      • Remove a specific claim
      • Get lemma on lexeme
      • Set lemma on lexeme
      • Add gloss to a sense on lexeme
      • Add form to a lexeme
      • Add a form or a sense to an existing lexeme
  • Other projects
• Installation
• Installation of the development environment
• Using a Wikibase instance
  • Wikimedia Foundation User-Agent policy
• The Core Parts
  • Entity manipulation
  • wbi_login
    • Login using OAuth1 or OAuth2
      • As a bot
      • To impersonate a user (OAuth 1.0a)
    • Login with a bot password
    • Login with a username and a password
  • Wikibase Data Types
  • Structured Data on Commons
    • Retrieve data
    • Write data
• More than Wikibase
• Helper Methods
  • Use MediaWiki API
  • Execute SPARQL queries
    • Wikimedia Commons Query Service
  • Wikibase search entities
  • Merge Wikibase items
  • Check a Wikibase instance
  • Other helpers
  • Track the edits with EditGroups
• Examples (in "normal" mode)
  • Create a new Item
  • Modify an existing item
  • A bot for Mass Import
  • Replace a property by another
• Examples (in "fast run" mode)
  • The base filter
  • Checking if a write is required
  • Options
  • Checking labels, descriptions and aliases
  • Limitations
  • Performance statistics
• Debugging

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 :

• A complete rewrite of the library with a more object-oriented architecture allowing for easy interaction, data validation and extended functionality
• Add support for reading and writing Lexeme, MediaInfo and Property entities
• Python 3.11 to 3.15 support, validated with unit tests
• Type hints implementation for arguments and return, checked with mypy static type checker
• Add OAuth 2.0 login method
• Add logging module support

WikibaseIntegrator also provides a "fast run" mode, which compares the local data with the live data through the SPARQL endpoint and only writes to the Wikibase instance when something actually changed (see Examples (in "fast run" mode)).

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')

aliases.add() adds one or more aliases while keeping the existing ones. An alias that is already present is left as it is.

entity.aliases.add('en', ['Item', 'Thing'])

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

• Initialize a new Qualifiers object, add a String qualifier with the property P828 and the value 'Item qualifier' to the Qualifiers object.
• Create a String claim with the property P31533 and the value 'A String property' with the previously created Qualifiers object as qualifiers.
• Add the newly created claim to the local entity.

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

• Initialize a new References object.
• Initialize a new Reference object, add a String reference with the property P828 and the value 'Item string reference' to the Reference object.
• Initialize a new Reference object, add a String reference with the property P828 and the value 'Another item string reference' to the Reference object.
• Add the newly created Reference objects to the References object.
• Create a String claim with the property P31533 and the value 'A String property' with the previously created References object as references.
• Add the newly created claim to the local entity.

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)

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

• Create a new Sense object.
• Add an English gloss with the value 'English gloss' to the Sense object.
• Add a French gloss with the value 'French gloss' to the Sense object.
• Create a String claim with the property P828 and the value 'Create a string claim for sense'.
• Add the newly created claim to the Sense object.
• Add the newly created Sense object to the 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

• Create a new Form object.
• Add an English form representation with the value 'English form representation' to the Form object.
• Add a French form representation with the value 'French form representation' to the Form object.
• Create a String claim with the property P828 and the value 'Create a string claim for form'.
• Add the newly created claim to the Form object.
• Add the newly created Form object to the 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)

Add a form or a sense to an existing lexeme

Contrary to write(), write_form() and write_sense() only send the new Form or Sense to the Wikibase instance (with the wbladdform and wbladdsense actions), the rest of the lexeme is left untouched. They return the id assigned by the instance, which is also set on the object.

write_forms() and write_senses() add every Form or Sense of the lexeme without an id, one request per Form or Sense.

From lexeme_write.ipynb

lexeme = wbi.lexeme.get('L5')

form = Form()
form.representations.set(language='en', value='English form representation')
form.grammatical_features = ['Q146786']
lexeme.write_form(form)  # 'L5-F3'

sense = Sense()
sense.glosses.set(language='en', value='English gloss')
lexeme.senses.add(sense)
lexeme.write_senses()  # ['L5-S2']

Other projects

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

• https://github.com/ACMILabs/acmi-wikidata-bot - A synchronisation robot to push ACMI API Wikidata links to Wikidata.
• https://github.com/LeMyst/wd-population - Update French population on Wikidata
• https://github.com/SisonkeBiotik-Africa/AfriBioML - Resources for developing a bibliometric study on machine learning for healthcare in Africa
• https://github.com/SisonkeBiotik-Africa/Relational-NER - A Python code for enhancing the output of multilingual named entity recognition based on Wikidata relations
• https://github.com/SoftwareUnderstanding/SALTbot - Software and Article Linker Toolbot
• https://github.com/dpriskorn/ItemSubjector - CLI-tool to easily add "main subject" aka topics in bulk to groups of items on Wikidata
• https://github.com/dpriskorn/hiking_trail_matcher - Script that helps link together hiking trails in Wikidata and OpenStreetMap
• https://github.com/eoan-ermine/wikidata_statistik_population - Update German population on Wikidata
• https://github.com/internetarchive/cgraphbot - Wikibase bot for updating identifiers and citation relationships
• https://github.com/lubianat/ibge_2021_to_wikidata - Update Population of Brazilian Cities
• https://github.com/lcnetdev/lccn-wikidata-bot - Adding LCCNs (Library of Congress Control Number) from NACO (Name Authority Cooperative Program) to Wikidata
• https://github.com/dpriskorn/WikidataEurLexScraper - Improve all Eur-Lex items in Wikidata based on information scraped from Eur-Lex
• https://github.com/dpriskorn/LexDanNet - Help link DanNet 2.2 word ID with Danish Wikidata lexemes
• https://github.com/lubianat/kudos_wikibase
• https://github.com/dlindem/wikibase

Installation

The easiest way to install WikibaseIntegrator is to use the pip package manager. WikibaseIntegrator supports Python 3.11 and above.

python -m pip install wikibaseintegrator

You can also clone the repository and install it, preferably in a virtualenv.

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

cd WikibaseIntegrator

python -m pip install --upgrade pip setuptools

python -m pip install .

If you want to install a specific branch or tag from the repository, you can use the following command:

python -m pip install git+https://github.com/LeMyst/WikibaseIntegrator.git@v0.13.0

where v0.13.0 is the tag or branch you want to 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())

Installation of the development environment

The following commands will install the development environment with the necessary dependencies:

python -m poetry install --with dev,coverage

The docs and notebooks groups are also available (--with docs,notebooks) to build the documentation and run the Jupyter notebooks.

The unit tests run offline (the HTTP calls are mocked) with:

python -m pytest

The integration tests, running against a real Wikibase instance, are deselected by default and can be run with python -m pytest -m integration (see test/integration/README.md).

Optionally, pre-commit can run the lint checks of the CI before each commit (isort, flynt, codespell) and before each push (mypy, pylint), using the tools installed by Poetry:

pipx install pre-commit  # or use "uvx pre-commit"
pre-commit install

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:

• wikibaseintegrator.entities.item.ItemEntity
• wikibaseintegrator.entities.property.PropertyEntity
• wikibaseintegrator.entities.lexeme.LexemeEntity
• wikibaseintegrator.entities.mediainfo.MediaInfoEntity (beta, see Structured Data on Commons)

Features:

• Loading a Wikibase entity based on its Wikibase entity ID.
• All Wikibase data types are implemented (and some data types implemented by extensions).
• Full access to the entire Wikibase entity in the form of a JSON dict representation.

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 are multiple methods to login:

• wbi_login.OAuth2(consumer_token, consumer_secret) (recommended)
• wbi_login.OAuth1(consumer_token, consumer_secret, access_token, access_secret)
• wbi_login.Clientlogin(user, password)
• wbi_login.Login(user, password)

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.OAuth1. 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.OAuth1 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 18 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 wikibaseintegrator.datatypes.

The data types currently implemented:

• CommonsMedia
• EntitySchema
• ExternalID
• Form
• GeoShape
• GlobeCoordinate
• Item
• Lexeme
• Math
• MonolingualText
• MusicalNotation
• Property
• Quantity
• Sense
• String
• TabularData
• Time
• URL

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

• extra.EDTF (Wikibase EDTF)
• extra.LocalMedia (Wikibase Local Media)

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.

The datatype names used by the Wikibase API are listed in the wikibaseintegrator.wbi_enums.WikibaseDatatype enum, e.g. for a new property: wbi.property.new(datatype=WikibaseDatatype.ITEM). Its members are also strings equal to the API names (WikibaseDatatype.ITEM == 'wikibase-item'), so a plain string works too. Each data type class gives its own name in DTYPE (Item.DTYPE is WikibaseDatatype.ITEM). A datatype unknown to WikibaseIntegrator, provided by a Wikibase extension, is kept as a string.

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()

To query Structured Data on Commons with SPARQL, or to use the fast run mode with MediaInfo entities, see Wikimedia Commons Query Service.

More than Wikibase

WikibaseIntegrator natively supports some extensions:

• MediaInfo entity (beta, see Structured Data on Commons) - WikibaseMediaInfo
• EDTF datatype - Wikibase EDTF
• LocalMedia datatype - Wikibase Local Media
• Lexeme entity and datatype - WikibaseLexeme

When a Lexeme request (getting or writing a lexeme, form or sense) is sent to a Wikibase instance without lexeme support, a wikibaseintegrator.wbi_exceptions.MissingExtensionError naming the missing WikibaseLexeme extension is raised, instead of the generic error returned by the API.

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.)

If the SPARQL endpoint is protected, pass the credentials with auth, either a (username, password) tuple for HTTP Basic authentication or any requests.auth.AuthBase instance, or add your own HTTP headers with headers. To set the credentials once for every query, including the ones executed by the fast run mode, use wbi_config['SPARQL_AUTH']:

from wikibaseintegrator import wbi_helpers
from wikibaseintegrator.wbi_config import config as wbi_config

query = 'SELECT ?item WHERE { ?item ?p ?o . } LIMIT 1'

wbi_helpers.execute_sparql_query(query, auth=('username', 'password'))
wbi_helpers.execute_sparql_query(query, headers={'Authorization': 'Bearer <token>'})

wbi_config['SPARQL_AUTH'] = ('username', 'password')
wbi_helpers.execute_sparql_query(query)

Wikimedia Commons Query Service

The SPARQL endpoint of Structured Data on Commons, the Wikimedia Commons Query Service (WCQS), authenticates with a long-lived wcqsOauth cookie. There is no API to get it: copy it from a web browser logged in to WCQS, as explained in Commons:SPARQL query service/API endpoint. wbi_login.WCQSAuth sends it, and keeps the short-lived session cookie WCQS sets in return:

from wikibaseintegrator import wbi_helpers
from wikibaseintegrator.wbi_config import config as wbi_config
from wikibaseintegrator.wbi_login import WCQSAuth

wbi_config['SPARQL_ENDPOINT_URL'] = 'https://commons-query.wikimedia.org/sparql'
wbi_config['WIKIBASE_URL'] = 'https://commons.wikimedia.org'
# Every query, including the ones of the fast run mode, is authenticated
wbi_config['SPARQL_AUTH'] = WCQSAuth('<wcqsOauth cookie>')

wbi_helpers.execute_sparql_query('SELECT ?file WHERE { ?file wdt:P180 wd:Q146 } LIMIT 10')

An invalid or revoked cookie raises a WCQSAuthenticationError, instead of being retried.

Wikibase search entities

The method wbi_helpers.search_entities() allows for string search in a Wikibase instance, using the labels and aliases of the entities. It takes the search string (search_string) and optional parameters: the language of the search (language, default wbi_config['DEFAULT_LANGUAGE']), strict_language to disable the language fallback, the type of entity to search (search_type: item, property, lexeme, form, sense or mediainfo, default item), the maximum number of results (max_results, default 50, higher values trigger additional paginated calls) and dict_result to return detailed dictionaries instead of a list of IDs. Like the other helpers, it also accepts mediawiki_api_url, login, user_agent, etc.

from wikibaseintegrator import wbi_helpers

print(wbi_helpers.search_entities(search_string='Earth', max_results=5))

Merge Wikibase items

Sometimes, Wikibase items need to be merged. An API call exists for that, and wbi_helpers.merge_items() implements it. It takes these arguments:

• the QID of the item which should be merged into another item (from_id)
• the QID of the item the first item should be merged into (to_id)
• a login object of type wbi_login.Login to provide the API call with the required authentication information
• a list of elements to ignore conflicts for (ignore_conflicts), which can contain 'description', 'sitelink' and 'statement'. This will do a partial merge for the elements which do not conflict and should generally be avoided because it leaves a crippled item in Wikibase. Before a merge, any potential conflicts should be resolved first.
• a boolean if the changes need to be marked as made by a bot (is_bot)
• a list of change tags to apply to the revision (tags)

Wikibase turns the emptied item into a redirect to the target item, so there is no need to create the redirect separately.

To merge more than two items at once, wbi_helpers.merge_multiple_items() takes a list of QIDs and merges all of them into the one with the lowest numeric ID, which it returns. It accepts the same login, ignore_conflicts, is_bot and tags arguments. The merges are done one by one: if one fails, its exception is raised and the merges already done are kept.

from wikibaseintegrator import wbi_helpers, wbi_login

login = wbi_login.Login(user='<bot user name>', password='<bot password>')
to_id = wbi_helpers.merge_multiple_items(['Q100', 'Q30', 'Q5'], login=login)  # Q30 and Q100 are merged into Q5

Check a Wikibase instance

check_wikibase() reports the features of a Wikibase instance: the MediaWiki version, the installed extensions, the entity types and the datatypes it supports, and whether the SPARQL endpoint answers. It also checks that config['WIKIBASE_URL'] matches the concept URIs of the instance, as the SPARQL queries built by WikibaseIntegrator (fast run mode, ...) rely on it.

from wikibaseintegrator.wbi_config import config as wbi_config
from wikibaseintegrator.wbi_helpers import check_wikibase

wbi_config['MEDIAWIKI_API_URL'] = 'https://my-wikibase.example.org/w/api.php'
wbi_config['SPARQL_ENDPOINT_URL'] = 'https://my-wikibase.example.org/query/sparql'
wbi_config['WIKIBASE_URL'] = 'https://my-wikibase.example.org'

result = check_wikibase()
print(result)  # A readable report, with the problems found

if not result.has_lexemes:
    print('This instance does not support lexemes')

The result also provides has_mediainfo, has_search (WikibaseCirrusSearch, needed by the haswbstatement search keyword), has_quality_constraints (needed by check_constraints()), extensions, entity_types, datatypes and problems.

Other helpers

wbi_helpers also provides:

• merge_lexemes(): merge two lexemes.
• remove_claims(): delete a claim from an entity by its claim ID.
• check_constraints(): check the constraint violations of entities or claims with the WikibaseQualityConstraints extension.
• fulltext_search(): perform a fulltext search on the MediaWiki instance.
• delete_page(): delete a page.
• download_entity_ttl(): download the RDF (Turtle) representation of an entity.

Some examples are available in the wbi_helpers.ipynb notebook.

Track the edits with EditGroups

EditGroups tracks the edits of a bot on Wikidata as a batch, which can be reviewed, discussed and reverted at once. An edit is part of a batch when its summary ends with a link to the batch, which EditGroup generates:

from wikibaseintegrator import WikibaseIntegrator
from wikibaseintegrator.datatypes import Item
from wikibaseintegrator.wbi_editgroups import EditGroup

wbi = WikibaseIntegrator(login=login_instance)

# One EditGroup per batch, with a random batch ID
editgroup = EditGroup()
print(editgroup.url)  # https://editgroups.toolforge.org/b/CB/<batch ID>/

for qid in ['Q582', 'Q90']:
    item = wbi.item.get(qid)
    item.claims.add(Item(value='Q484170', prop_nr='P31'))
    # "Add instance of ([[:toolforge:editgroups/b/CB/<batch ID>|details]])"
    item.write(summary=editgroup.summary('Add instance of'))

To add edits to an existing batch, for example after a restart of the bot, pass its ID: EditGroup('89ead4fe'). A summary that is too long is shortened, so that the link is never truncated.

Examples (in "normal" mode)

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

• A datatype object containing a value.
• An entity object (Item/Property/Lexeme/...) which takes the data, does the checks and performs write.

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)

# Add the claims, the existing claims with the same property are kept
item.claims.add(data)

# To replace all the existing claims with the same property instead, use ActionIfExists.REPLACE_ALL
# item.claims.add(data, action_if_exists=ActionIfExists.REPLACE_ALL)

item.write()

A bot for Mass Import

An enhanced example of the previous bot puts the creation of the data and the entity 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()

Replace a property by another

This bot moves every statement using a property (OLD_PROPERTY) to another property with the same datatype (NEW_PROPERTY), on all the entities of a Wikibase instance. Each statement is copied with its value, qualifiers, references and rank to the new property, then the original statement is removed, in a single edit per entity.

from copy import deepcopy

from wikibaseintegrator import WikibaseIntegrator, wbi_login
from wikibaseintegrator.wbi_config import config as wbi_config
from wikibaseintegrator.wbi_helpers import execute_sparql_query, generate_entity_instances

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

OLD_PROPERTY = 'P1234'  # The property to replace
NEW_PROPERTY = 'P5678'  # The property to use instead
DRY_RUN = True  # Set to False to write the changes

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

wbi = WikibaseIntegrator(login=login_instance)

# The values are copied as they are, so both properties must use the same datatype
old_datatype = wbi.property.get(OLD_PROPERTY).datatype
new_datatype = wbi.property.get(NEW_PROPERTY).datatype
if old_datatype != new_datatype:
    raise ValueError(f'{OLD_PROPERTY} ({old_datatype}) and {NEW_PROPERTY} ({new_datatype}) use different datatypes')

# Find the entities holding at least one statement with the old property
query = f'SELECT DISTINCT ?entity WHERE {{ ?entity <{wbi_config["WIKIBASE_URL"]}/prop/{OLD_PROPERTY}> ?statement . }}'
results = execute_sparql_query(query)
entity_ids = [result['entity']['value'].rsplit('/', 1)[-1] for result in results['results']['bindings']]
# The statements of the forms and senses of a lexeme (L1-F1, L1-S1) are not handled by this example
entity_ids = [entity_id for entity_id in entity_ids if '-' not in entity_id]
print(f'{len(entity_ids)} entities use {OLD_PROPERTY}')

# Retrieve the entities 50 by 50, the maximum of the wbgetentities API action
for start in range(0, len(entity_ids), 50):
    for entity_id, entity in generate_entity_instances(entities=entity_ids[start:start + 50], login=login_instance):
        for claim in entity.claims.get(OLD_PROPERTY):
            # Copy the statement to the new property, as a new statement
            new_claim = deepcopy(claim)
            new_claim.reset_id()
            new_claim.mainsnak.property_number = NEW_PROPERTY
            # Not added twice if the entity already holds the same value with the new property
            entity.claims.add(new_claim)

            # Remove the original statement
            claim.remove()

        if DRY_RUN:
            print(f'{entity_id}: {len(entity.claims.get(OLD_PROPERTY))} statement(s) to move')
        else:
            entity.write(summary=f'Replace {OLD_PROPERTY} by {NEW_PROPERTY}', is_bot=True)

Examples (in "fast run" mode)

The fast run mode is designed for bots synchronising an external resource with a Wikibase instance, where most of the entities are usually already up to date. Instead of loading every entity through the MediaWiki API to compare it with the local data, the fast run mode loads the current state of the entities of the data corpus with paginated SPARQL queries, then decides locally, entity per entity, if a write is required. When nothing changed (the most common case), no MediaWiki API call is made at all for that entity.

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 on entities without a known entity ID, the data you provide must contain at least one unique value/id only present on one Wikibase entity, e.g. an NCBI entrez gene ID, Uniprot ID, etc. This unique value is used to identify the target entity. If the entity ID is already known (e.g. the entity was loaded with wbi.item.get()), the comparison is automatically restricted to that entity and this requirement does not apply.

The base filter

The base filter defines the data corpus and is a list of datatype instances (the same classes used to create claims). Three forms are supported:

• A property with a value: the entities must have this exact statement, e.g. Item(prop_nr='P703', value='Q15978631') (found in taxon Homo sapiens).
• A property without a value: the entities must have a statement with this property, whatever the value, e.g. ExternalID(prop_nr='P351') (any NCBI Entrez Gene ID).
• A property path, given as a list of two datatype instances: the value is reached through the first property followed by any number of hops with the second one, e.g. [Item(prop_nr='P31', value='Q11173'), Item(prop_nr='P279')] (instance of (P31) chemical compound (Q11173), directly or through a chain of subclass of (P279)).

from wikibaseintegrator.datatypes import ExternalID, Item

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

Checking if a write is required

The entry point is entity.write_required(). It returns True if the local entity differs from the live data and an actual write is needed, False if the entity is already up to date and the write can be skipped. The full example, a modified version of the mass import bot from the normal mode example:

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

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

wbi = WikibaseIntegrator(login=login)

fast_run_base_filter = [ExternalID(prop_nr='P351'), ExternalID(prop_nr='P704'), Item(prop_nr='P703', value='Q15978631')]

# We have raw data, which should be written to Wikidata, namely two human NCBI entrez gene IDs mapped to two Ensembl transcript 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)
        ]
    ]

    # data type objects
    entrez_gene_id = ExternalID(value=entrez_id, prop_nr='P351', references=references)
    ensembl_transcript_id = ExternalID(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]

    item = wbi.item.new()
    item.claims.add(data)

    # Compare the local data with the live data and only write when something differs
    if item.write_required(base_filter=fast_run_base_filter):
        item.write()

Note: only the claims whose property appears in the base filter are compared. In the example above, the P351 and P704 claims are checked because both properties are part of the base filter; a claim on any other property would be ignored by the comparison. A write is reported as not required only when one entity (the entity being edited, when its ID is known) holds all the compared claims.

Options

write_required() accepts optional parameters, forwarded to the fastrun container:

• use_qualifiers (default True): also compare the qualifiers of the statements.
• use_references (default False): also compare the references of the statements. By default, references are ignored.
• use_rank (default False): also compare the rank of the statements.
• case_insensitive (default False): compare string values case-insensitively. The comparison of qualifiers, references and ranks stays case sensitive.
• cache (default True): keep the data returned by the SPARQL endpoint in memory and reuse it for the next checks. When disabled, the queries are restricted to the exact values of the claims instead of preloading every statement of the property.
• action_if_exists (default ActionIfExists.REPLACE_ALL): the action that will be used for the write. With FORCE_APPEND, the statements are always appended and a write is always reported as required. With the other actions, the claims must already exist on the entity for the write to be skipped.

The preloaded data is shared: containers are cached at the module level and reused by every write_required() call using the same base filter and options, so the SPARQL queries are only executed once per property.

Checking labels, descriptions and aliases

write_required() only compares claims. To check language data (labels, descriptions or aliases), use the fastrun container directly:

from wikibaseintegrator import wbi_fastrun
from wikibaseintegrator.datatypes import ExternalID, Item

frc = wbi_fastrun.get_fastrun_container(base_filter=[ExternalID(prop_nr='P351'), Item(prop_nr='P703', value='Q15978631')])

# Resolve the entity ID from a unique value, without any MediaWiki API call
qids = frc.get_entities(claims=[ExternalID(value='50943', prop_nr='P351')])

# Returns True if the English label differs from 'CDK7' and a write is required
frc.check_language_data(qid=qids[0], lang_data=['CDK7'], lang='en', lang_data_type='label')

Limitations

• The SPARQL endpoint can lag behind the live data (typically a few seconds to a few minutes on Wikidata). A write that just happened may not be visible yet, and the same write could be reported as required again.
• The attributes missing from the SPARQL simple values are rebuilt with their default value: the bounds of a quantity, the precision of a globe coordinate, the timezone and calendar model of a time, etc. Statements using non-default attributes are always reported as requiring a write. The precision of a time and the unit of a quantity are loaded from the value nodes, so they are compared.
• EntitySchema has no PTYPE mapping to the Wikibase ontology, so its statements are never matched by the fastrun data and are always reported as requiring a write. Every other datatype implements from_sparql_value().
• The qualifiers, references and ranks are loaded lazily with one SPARQL query per statement to check. This is fast when most entities are up to date, but can slow down the run when many entities hold the same values.

Performance statistics

Measured on a synchronisation bot of French administrative entities with Wikidata. The "partial fastrun" column is the mode without cache, where the queries are restricted to the exact values of the claims:

Debugging

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

import logging

logging.basicConfig(level=logging.DEBUG)