Wikibase Integrator

• WikibaseIntegrator / WikidataIntegrator
• Installation
• Using a Wikibase instance
• The Core Parts
  • wbi_core.ItemEngine
  • wbi_core.FunctionsEngine
  • wbi_login.Login
    • Login using OAuth1 or OAuth2
    • Login with a username and a password
  • Wikibase Data Types
• Helper Methods
  • Execute SPARQL queries
  • Use Mediawiki API
  • Wikibase search entities
  • Merge Wikibase items
• Examples (in "normal" mode)
  • A Minimal Bot
  • A Minimal Bot for Mass Import
• Examples (in "fast run" mode)

WikibaseIntegrator / WikidataIntegrator

WikibaseIntegrator (wbi) is a fork from WikidataIntegrator (wdi) whose purpose is to be focused on Wikibase compatibility. There have been many improvements that have led to breaking changes in the code. Refer to the release notes to find out what has changed.

Installation

The easiest way to install WikibaseIntegrator is using pip. WikibaseIntegrator supports Python 3.7 and higher. If Python 2 is installed pip will lead to an error indicating missing dependencies.

pip install wikibaseintegrator

You can also clone the repo and execute with administrator rights or install into a virtualenv.

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

cd WikibaseIntegrator

python -m pip install pip setuptools

python setup.py install

To test for correct installation, start a Python console and execute the following (Will retrieve the Wikidata item for 'Human'):

from wikibaseintegrator import wbi_core

my_first_wikidata_item = wbi_core.ItemEngine(item_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_representation())

Using a Wikibase instance

WikibaseIntegrator use Wikidata as default endpoint. To use a Wikibase instance instead, you can overload the wbi_config.

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:8181/api.php'
wbi_config['SPARQL_ENDPOINT_URL'] = 'http://localhost:8989/bigdata/sparql'
wbi_config['WIKIBASE_URL'] = 'http://wikibase.svc'

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

The Core Parts

wbi_core supports two modes it can be operated in, a normal mode, updating each item at a time and, a fast run mode, which is pre-loading data locally and then just updating items if the new data provided is differing from what is in Wikidata. The latter mode allows for great speedups (measured up to 9x) when tens of thousand of Wikidata items need to be checked if they require updates but only a small number will finally be updated, a situation usually encountered when keeping Wikidata in sync with an external resource.

wbi_core consists of a central class called ItemEngine and Login for authenticating with a MediaWiki isntance (like Wikidata).

wbi_core.ItemEngine

This is the central class which does all the heavy lifting.

Features:

• Load a Wikibase item based on data to be written (e.g. a unique central identifier)
• Load a Wikibase item based on its Wikibase item id (aka QID)
• Checks for conflicts automatically (e.g. multiple items carrying a unique central identifier will trigger an exception)
• Checks automatically if the correct item has been loaded by comparing it to the data provided
• All Wikibase data types implemented
• A dedicated wbi_core.ItemEngine.write() method allows loading and consistency checks of data before any write to Wikibase is performed
• Full access to the whole Wikibase item as a JSON document

There are two ways of working with Wikibase items:

• A user can provide data, and ItemEngine will search for and load/modify an existing item or create a new one, solely based on the data provided (preferred). This also performs consistency checks based on a set of SPARQL queries.
• A user can work with a selected QID to specifically modify the data on the item. This requires that the user knows what he/she is doing and should only be used with great care, as this does not perform consistency checks.

Examples below illustrate the usage of ItemEngine.

wbi_core.FunctionsEngine

wbi_core.FunctionsEngine provides a set of static functions to request or manipulate data from MediaWiki API or SPARQL Service.

Features:

• Minimize the number of HTTP requests for reads and writes to improve performance
• Method to easily execute SPARQL queries on the Wikibase SPARQL endpoint.

wbi_login.Login

Login using OAuth1 or OAuth2

OAuth is the authentication method recommended by the Mediawiki developpers. It can be used for authenticating 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. You will get a consumer key, consumer secret, access token and access secret.

Example if you use OAuth 1.0a:

from wikibaseintegrator import wbi_login

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

Example if you use OAuth 2.0:

from wikibaseintegrator import wbi_login

login_instance = wbi_login.Login(client_id='<your_client_app_key>', client_secret='<your_client_app_secret>')

To impersonate a user (OAuth 1.0a)

If WBI should be used as a backend for a webapp, the script should use OAuth for authentication, WBI supports this, you just need to specify consumer key and consumer secret when instantiating wbi_login.Login. In contrast to username and password login, OAuth is a 2 steps process as manual user confirmation for OAuth login is required. This means that the method wbi_login.Login.continue_oauth() needs to be called after creating the wbi_login.Login instance.

Example:

from wikibaseintegrator import wbi_login

login_instance = wbi_login.Login(consumer_key='<your_consumer_key>', consumer_secret='<your_consumer_secret>')
login_instance.continue_oauth()

The method wbi_login.Login.continue_oauth() will either prompt the user for a callback URL (normal bot runs), or it will take a parameter so in the case of WBI being used as a backend for e.g. a web app, where the callback will provide the authentication information directly to the backend and so no copy and paste of the callback URL is required.

Login with a username and a password

wbi_login.Login provides the login functionality and also stores the cookies and edit tokens required (For security reasons, every Mediawiki edit requires an edit token). The constructor takes two essential parameters, username and password. Additionally, the server (default wikidata.org), and the token renewal periods can be specified. 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>', pwd='<bot password>')     

Wikibase Data Types

Currently, Wikibase supports 17 different data types. The data types are represented as their own classes in wbi_core. Each data types has its specialties, which means that some of them require special parameters (e.g. Globe Coordinates).

The data types currently implemented:

• wbi_core.CommonsMedia
• wbi_core.ExternalID
• wbi_core.Form
• wbi_core.GeoShape
• wbi_core.GlobeCoordinate
• wbi_core.ItemID
• wbi_core.Lexeme
• wbi_core.Math
• wbi_core.MonolingualText
• wbi_core.MusicalNotation
• wbi_core.Property
• wbi_core.Quantity
• wbi_core.Sense
• wbi_core.String
• wbi_core.TabularData
• wbi_core.Time
• wbi_core.Url

For details of how to create values (=instances) with these data types, please (for now) consult the docstrings in the source code. Of note, these data type instances hold the values and, if specified, data type instances for references and qualifiers. Furthermore, calling the get_value() method of an instance returns either an integer, a string or a tuple, depending on the complexity of the data type.

Helper Methods

Execute SPARQL queries

The method wbi_core.ItemEngine.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 entpoint 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.)

Use Mediawiki API

The method wbi_core.FunctionsEngine.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_core

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

print(wbi_core.FunctionsEngine.mediawiki_api_call_helper(query, allow_anonymous=True))

Wikibase search entities

The method wbi_core.ItemEngine.get_search_results() 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_core.FunctionsEngine.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 server (mediawiki_api_url) if the Wikibase instance is not Wikidata and a flag for ignoring merge conflicts (ignore_conflicts). The last parameter 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.

Examples (in "normal" mode)

A Minimal Bot

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

• A login object, as described above.
• A data type object containing a value.
• A ItemEngine object which takes the data, does the checks and performs write.

from wikibaseintegrator import wbi_core, wbi_login

# login object
login_instance = wbi_login.Login(user='<bot user name>', pwd='<bot password>')

# data type object, e.g. for a NCBI gene entrez ID
entrez_gene_id = wbi_core.String(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 for and then edit/create new item
wd_item = wbi_core.ItemEngine(data=data)
wd_item.write(login_instance)

A Minimal 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 wbi_core, wbi_login

# login object
login_instance = wbi_login.Login(user='<bot user name>', pwd='<bot password>')

# 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'
}

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

    # data type object
    entrez_gene_id = wbi_core.String(value=entrez_id, prop_nr='P351', references=references)
    ensembl_transcript_id = wbi_core.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
    wd_item = wbi_core.ItemEngine(data=data)
    wd_item.write(login_instance)

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 item, 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:

fast_run_base_filter = {'P351': '', 'P703': 'Q15978631'}

The full example:

from wikibaseintegrator import wbi_core, wbi_login

# login object
login_instance = wbi_login.Login(user='<bot user name>', pwd='<bot password>')

fast_run_base_filter = {'P351': '', 'P703': '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 = [
        [
            wbi_core.ItemID(value='Q20641742', prop_nr='P248', is_reference=True),
            wbi_core.Time(time='+2020-02-08T00:00:00Z', prop_nr='P813', is_reference=True),
            wbi_core.ExternalID(value='1017', prop_nr='P351', is_reference=True)
        ]
    ]

    # data type object
    entrez_gene_id = wbi_core.String(value=entrez_id, prop_nr='P351', references=references)
    ensembl_transcript_id = wbi_core.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
    wd_item = wbi_core.ItemEngine(data=data, fast_run=fast_run, fast_run_base_filter=fast_run_base_filter)
    wd_item.write(login_instance)

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