This is a validator for HTML-embedded metadata standards. It knows the location of the official schema definitions, and uses these documents as validation templates. As a contributor, you can easily subclass the base validator class to plug into this functionality.

To see the validator in action:

$ pip install schemato
$ ipython
>>> from schemato import Schemato
>>> sc = Schemato("../test_documents/rdf.html", loglevel="INFO")
>>> sc.validate()

The first time you run schemato, it will make requests for the latest versions of the official schema definitions. These files are cached locally with a fairly long expiry, to avoid the overhead of web requests. Schemato will then call the validate() method of the Validator subclasses listed in settings.py.

There are a few other test documents available for validation in the test_documents subdirectory.

Schemato's distiller framework lets you implement strategies for creating a "normalized" set of metadata by mixing and matching metadata from different supported standards.

Supported so far:

* parsely-page
* OpenGraph
* Schema.org NewsArticle

Take a look at the clean Python class definitions that describe the strategies:

https://github.com/Parsely/schemato/blob/master/schemato/distillery.py

There are two examples -- one that tries pp and falls back on Schema.org/OpenGraph (called ParselyDistiller) and another the tries Schema.org and falls back on OpenGraph (called NewsDistiller).

The distiller returns a clean Python dictionary that has all the extracted fields, as well as a dictionary describing which metadata standard was used to source each field. The framework is defined here:

https://github.com/Parsely/schemato/blob/master/schemato/distillers.py

Here is an example of usage:

>>> from schemato import Schemato
>>> from distillery import ParselyDistiller, NewsDistiller
>>> mashable = Schemato("http://mashable.com/2012/10/17/iphone-5-supply-problems/")
>>> ParselyDistiller(mashable).distill()
{'author': u'Seth Fiegerman',
'image_url': u'http://5.mshcdn.com/wp-content/uploads/2012/10/iphone-lineup.jpg',
'link': u'http://mashable.com/2012/10/17/iphone-5-supply-problems/',
'page_type': u'post',
'post_id': u'1432059',
'pub_date': u'2012-10-17T11:36:40+00:00',
'section': u'bus',
'site': 'Mashable',
'title': u"Apple's Manufacturing Partner Explains iPhone 5 Supply Problems"}

In this case, Mashable implements the parsely-page metadata field, which is used to source all the defined properties for this distiller.

>>> d = NewsDistiller(mashable)
>>> d.distill()
{'author': None,
'id': None,
'image_url': 'http://5.mshcdn.com/wp-content/uploads/2012/10/iphone-lineup.jpg',
'link': 'http://mashable.com/2012/10/17/iphone-5-supply-problems/',
'pub_date': None,
'section': None,
'title': "Apple's Manufacturing Partner Explains iPhone 5 Supply Problems"}
>>> d.sources
{'author': None,
'id': None,
'image_url': 'og:image',
'link': 'og:url',
'pub_date': None,
'section': None,
'title': 'og:title'}

In this case, our strategy did not involve parsely-page, and instead used Schema.org and OpenGraph. Since Mashable does not implement Schema.org but does implement OpenGraph, it comes up with the fields it can. The sources property shows which fields were populated and how they got their values.