Heuristics for Fixing Common Errors in Deployed schema.org Microdata

Heuristics for Fixing Common Errors
in Deployed schema.org Microdata
Robert Meusel and Heiko Paulheim

2
Motivation
Heuristics for Fixing Common Errors in Deployed schema.org Microdata - Meusel and Paulheim - ESWC 2015

3
Microdata in a Nutshell
 Adding structured information to web pages
• By marking up contents and entities
 Arbitrary vocabularies are possible
• Practically, only schema.org is deployed on a large scale
• Plus its historical predecessor: data-vocabulary.org
 Similar to RDFa
<div itemscope itemtype="http://schema.org/PostalAddress">
<span itemprop="name">Data and Web Science Group</span>
<span itemprop="addressLocality">Mannheim</span>,
<span itemprop="postalCode">68131</span>
<span itemprop="addressCountry">Germany</span>
</div>

4
Schema.org in a Nutshell
 Vocabulary for marking up entities on web pages
• 675 classes and 965 properties (as of May 2015, release 2.0)
 Promoted and consumes by major search engine companies
• Google, Bing, Yahoo!, and Yandex
• Google Rich Snippets
 Community-driven evolution and development
 Can be used with Microdata and RDFa
• Hardly used together with RDFa (<0.1% of RDFa-using websites [1])
[1] http://webdatacommons.org/structureddata/2014-12/stats/stats.html

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Schema.org in a Nutshell – Coverage
 Schema.org has incorporated some popular vocabularies, like:
• Good Relations (2012)
• W3C BibExtend (2014)
• MusicBrainz vocabulary (2015)
• Automotive Ontology (2015)

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Microdata with Schema.org in HTML Pages
<html>
…
<body>
…
<div id="main-section" class="performance left" data-
sku="M17242_580“>
<h1> Predator Instinct FG Fußballschuh
</h1>
<div>
<meta content="EUR">
<span
data-sale-price="219.95">219,95</span>
…
</body>
</html>
HTML pages embed directly
markup languages to annotate
items using different vocabularies
<html>
…
<body>
…
<div id="main-section" class="performance left" data-
sku="M17242_580" itemscope
itemtype="http://schema.org/Product">
<h1 itemprop="name"> Predator Instinct FG Fußballschuh
</h1>
<div itemscope itemtype="http://schema.org/Offer"
itemprop="offers">
<meta itemprop="priceCurrency" content="EUR">
<span itemprop="price" data-sale-
price="219.95">219,95</span>
…
</body>
</html>
1._:node1 <http://www.w3.org/1999/02/22-rdf-syntax-
ns#type> <http://schema.org/Product> .
2._:node1 <http://schema.org/Product/name> "Predator
Instinct FG Fußballschuh"@de .
3._:node1 <http://www.w3.org/1999/02/22-rdf-syntax-
ns#type> <http://schema.org/Offer> .
4._:node1 <http://schema.org/Offer/price>
"219,95"@de .
5._:node1 <http://schema.org/Offer/priceCurrency>
"EUR" .
6.…

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So Far, So Good …
 Schema is well explained on the schema.org websites
 Data providers are supported by validation tools
(e.g. Yandex structured data validator) when deploying
 Win-Win for both sides
 Plus: Data is (mostly) free accessible in the Web
…. but:
 >100.000s of data providers, which are mostly no schema.org
experts or evangelists
 Validators & schema might help but there is no need to use
them

8
So What Could Possibly Go Wrong?
 Usage of wrong namespaces
• http./schema.org
 Usage of undefined types
• http://schema.org/Breadcrumb
 Usage of undefined properties
• http://schema.org/postID
 Confusion of datatype properties and object properties
• _:n1 s:address “Jump Street 21”
 Property domain and range violations
• _:n1 a s:Product
_:n1 s:price “for free”

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Compiling a Schema.org Dataset
 Starting point: all pages in the CommonCrawl that contain
Microdata
 What could be (meant to be) schema.org?
• Everything that contains “schema.org” as substring in a namespace
• Everything that contains URIs where the protocol and authority is similar
to “http://schema.org/” (with an EditDistance of 1)
• Filter noise: removing all namespaces that occur only on one website
Final corpus consists of:
6.4 billion triples
extracted from over 217 billion pages
belonging to 398,542 data providers
which is 86% of all Microdata in the corpus.

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Namespace Violations
 More than 98% of the preselected pages use a correct
namespace
 Frequent namespace variations:
• http://www.schema.org/
• https://schema.org
• http:/schema.org
• http://SChema.org
Debated!

11
Undefined Types
 Used by around 6% of all data providers
 Typical causes:
• Misspellings: http://schema.org/Stores
• Miscapitalization: http://schema.org/localbusiness
 Comparison to LOD Compliance
• 5.8% of all Microdata documents
• 38.8% of all LOD documents (Hogan et al., 2010)
…/Store
…/LocalBusiness

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Undefined Properties
 Used by around 4% of all data providers
 Typical Causes:
• Miscapitalization: http://schema.org/contentURL
• Close but miss: http://schema.org/currency
http://schema.org/fax
• Made up: http://schema.org/blogId
http://schema.org/postId
…/contentUrl
…/priceCurrency

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Confusion of Object Properties with Data Properties
 i.e. using an object property with a string values
 Used by over 56.6% of all data providers
 Typical properties:
• http://schema.org/addresscountry
• http://schema.org/manufacturer
• http://schema.org/author
• http://schema.org/brand
• 8% of all LOD documents (Hogan et al., 2010)

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Confusion of Data Properties with Object Properties
 i.e. using a data property with a complex object
 Used by less than 0.2% of all data providers

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Property Domain Violations
 i.e. using a property with a subject not included in its domain
 Used by 4% of all data providers
 Typical violations are mainly shortcuts
• s:price used on s:Product
• s:streetAddress used on s:LocalBusiness
 Comparison to LOD Compliance:
• Difficult to compare as semantics are different
• List of schema.org domains is exhaustive
• LOD: open world assumption
s:Product  s:Offer  s:price
s:LocalBusiness  s:PostalAddress
 s:streetAddress

16
Data Property Range Violations
 i.e. using a data property with an incompatible literal
 Used by 9.6% of all data providers
 20 most common violations:
• 13 dates
• 3 Urls
• 2 numbers
• 2 times
 Comparison to LOD Compliance:
“a month ago”
“2 pieces”
“last week”

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Object Property Range Violations
 i.e. using an object property with a type outside its range
 Used by 8.6% of all data providers
 Typical violations:
• s:mainContentOfPage with s:Blog instead of
s:WebPageElement
Maybe a hint at a missing
hierarchy relation?

18
Schema.org Compliance Summary
 Surprisingly high level of compliance
 Providers are often not technology evangelists (unlike in LOD)
• Anybody can start publishing Microdata annotated HTML
 Most often higher than for LOD
• Except for the confusion of data and object properties
But still the number of erroneous pages could prevent data
consumers to make use of the annotated data and understand
the semantics.

19
Identifying and Fixing Wrong Namespaces
 Main errors due to missing slashes, wrong protocol and
capitalization
 Simple rules to handle wrong namespaces
• Removal of www
• Replacement of https by http
• Conversion to lower case
• Adding of missing slashes and removal of prefixes before schema.org
 Impact:
• 147 of 148 wrongly spelled namespaces could be fixed

20
Handling Undefined Types and Properties
 Main errors due to wrong capitalization
 Heuristic: Ignore capitalization when parsing entities from web
pages, and replace the schema element with the properly
capitalized version
 Impact (together with namespace fixes):
• Correct type replacement within 71% of all data providers
• Correct property replacement within 65% of all data providers
• Remaining data providers account for over 70% of all
undefined types and properties and
are hard-to-detect typos

21
Handling Object Properties with Literal Values
 Main objects modeled as literals are s:Organization,
s:Person and s:PostalAddress
 Manually inspecting those values for the object
properties s:author, s:creator and s:address
 Impact
• The heuristic could replace all misused
object properties on 92,449 data providers
• Might lead to changes in the type distribution
• E.g. 14 million new entities of type
s:PostalAddress
_:1 s:author “Robert” .
_:1 s:author _:2 .
_:2 a s:Person .
_:2 s:name “Robert” .

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Handling Property Domain Violations
 Main cause are shortcuts
 Heuristic to find the
property R and type T
for a domain violation
of property s:r:
One unique solution for only one of
the two patterns:
 Impact:
• 31% of erroneous data providers could be fixed
• No solution or multiple solutions for the rest
_:1 “5”
s:aggregatedRating
s:aggregatedRating
is not defined for
type of _:1
_:2
s:aggregatedRating
Type?
Property?
R s:domainIncludes s:t .
R s:rangeIncludes T .
s:r s:domainIncludes T .
R s:rangeIncludes s:t .
R s:domainIncludes T .
s:r s:domainIncludes T .

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Heuristics Summary
 Over 410 million wrong triples could be corrected
 Over 700 million missing triples could be added
 Corrections affected in total over 115.000 data providers
• ~ 28% of all data providers in the data set

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LD4IE Challenge @ ISWC 2015
Learn to annotate entities on HTML pages using already
annotated pages as training set.
 Deadline: 2015-07-15
 Challenge Page: goo.gl/laF6yl
 Contact: Heiko Paulheim
(heiko@dwslab.de)
Good Luck!

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Thank you! Questions? Feedback?
Data and more insights can be found at:
http://webdatacommons.org/structureddata/2013-
11/stats/fixing_common_errors.html
More interesting datasets and analysis can be found at the
website of WebDataCommons:
http://webdatacommons.org/index.html
Acknowledgement
The extraction and analysis of the datasets was supported
by AWS in Education Grant.

Heuristics for Fixing Common Errors in Deployed schema.org Microdata

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