Wikibase Integrator is a python package whose purpose is to manipulate data present on a Wikibase instance (like Wikidata).
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
- 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
- Common use cases
- Other projects
- Jupyter notebooks
- Installation
- Using a Wikibase instance
- The Core Parts
- More than Wikibase
- Helper Methods
- Examples (in "normal" mode)
- Examples (in "fast run" mode)
- Debugging
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 3.9 to 3.13 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
But WikibaseIntegrator lack the "fastrun" functionality implemented in WikidataIntegrator.
A (basic) documentation generated from the python source code is available on the Read the Docs website.
You can find some sample code (adding an entity, a lexeme, etc.) in the Jupyter notebook directory of the repository.
Get the entity with the QID Q582 from the instance.
From import_entity.ipynb
entity = wbi.item.get('Q582')Start a new local entity.
entity = wbi.item.new()Write a local entity to the instance.
From import_entity.ipynb
entity.write()Add an English and a French label to the local entity.
entity.labels.set('en', 'New item')
entity.labels.set('fr', 'Nouvel élément')Get the english label value of the local entity.
From item_get.ipynb
entity.labels.get('en').valueAdd an English and a French alias to the local entity.
entity.aliases.set('en', 'Item')
entity.aliases.set('fr', 'Élément')Add an English and a French description to the local entity.
entity.descriptions.set('en', 'A freshly created element')
entity.descriptions.set('fr', 'Un élément fraichement créé')Add a Time claim with the property P74 and the current time to the local entity.
claim_time = datatypes.Time(prop_nr='P74', time='now')
entity.claims.add(claim_time)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']- 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.
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)- 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.
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 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 all French lemmas of the lexeme.
lexeme.lemmas.get(language='fr')Add a French lemma with the value 'réponse' to the lexeme.
From lexeme_update.ipynb
lexeme.lemmas.set(language='fr', value='réponse')- 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)- 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)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
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 wikibaseintegratorYou 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 installTo 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())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
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)'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.
WikibaseIntegrator supports the manipulation of Item, Property, Lexeme and MediaInfo entities through these classes:
- wikibaseintegrator.entities.item.Item
- wikibaseintegrator.entities.property.Property
- wikibaseintegrator.entities.lexeme.Lexeme
- wikibaseintegrator.entities.mediainfo.MediaInfo
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 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:
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.
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.
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>')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.
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>')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>')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:
- CommonsMedia
- 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.
WikibaseIntegrator supports SDC (Structured Data on Commons) to update a media file hosted on Wikimedia Commons.
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'])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()WikibaseIntegrator natively supports some extensions:
- MediaInfo entity - WikibaseMediaInfo
- EDTF datatype - Wikibase EDTF
- LocalMedia datatype - Wikibase Local Media
- Lexeme entity and datatype - WikibaseLexeme
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))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.)
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.
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:
- 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 boolean if the changes need to be marked as made by a bot (is_bot)
- a flag for ignoring merge conflicts (ignore_conflicts), will do a partial merge for all statements which do not conflict. This should generally be avoided because it leaves a crippled item in Wikibase. Before a merge, any potential conflicts should be resolved first.
In order to create a minimal bot based on wbi_core, 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.
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()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()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()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.
You can enable debugging by adding this piece of code to the top of your project:
import logging
logging.basicConfig(level=logging.DEBUG)