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![8
Wrap-Up
A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary over Time - WIMS 2015
Semantic annotations are used by more and more websites
Entities on websites become machine-readable and machine-
understandable
schema.org together with Microdata is a success story
• Promoted by search engine companies
• Deployed by over 17% of all websites [1] (over 700k data providers)
Usage is more compliant to the schema than e.g. LOD [2]
[1] http://webdatacommons.org/structureddata/2014-12/stats/stats.html
[2] Meusel and Paulheim, ESWC 2015](https://image.slidesharecdn.com/webscalestudyoftheadoptionandevolutionoftheschemaorgvocabovertime-150720072354-lva1-app6891/75/A-Web-scale-Study-of-the-Adoption-and-Evolution-of-the-schema-org-Vocabulary-over-Time-8-2048.jpg)
![11
A Diachronic Perspective
Three releases of WDC Microdata corpus [1]
• 2012, 2013, and 2014
Versions of schema.org that were valid
• At the beginning of the crawl
• At the end of the crawl
A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary over Time - WIMS 2015
[1] http://webdatacommons.org/structureddata](https://image.slidesharecdn.com/webscalestudyoftheadoptionandevolutionoftheschemaorgvocabovertime-150720072354-lva1-app6891/75/A-Web-scale-Study-of-the-Adoption-and-Evolution-of-the-schema-org-Vocabulary-over-Time-11-2048.jpg)
![17
Bottom-up Evolution
Martin Luther
• Started the protestant church
• A success story, too (like schema.org)
• (i.e., 800 million adopters worldwide)
Famous quote:
• “Man muss […] dem gemeinen Mann aufs Maul schauen”
• (roughly:
“You have to listen to the way the common man really speaks.”)
A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary over Time - WIMS 2015
Martin Luther,
1483-1546
Disclaimer:
I do not speak for the
protestant church.](https://image.slidesharecdn.com/webscalestudyoftheadoptionandevolutionoftheschemaorgvocabovertime-150720072354-lva1-app6891/75/A-Web-scale-Study-of-the-Adoption-and-Evolution-of-the-schema-org-Vocabulary-over-Time-17-2048.jpg)
Uploaded byRobert Meusel
A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary over Time
This document presents a web-scale study on the adoption and evolution of the schema.org vocabulary over time, highlighting its increasing usage across websites and its support from major search engines. It discusses the mechanisms of microdata, the structure of schema.org, adoption trends, and the influences driving its success. The study concludes that both top-down and bottom-up adoption strategies have contributed to the growing homogeneity and compliance of schema.org implementations in web content.
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A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary over Time
- 1. A Web-scale Studyof the Adoption and Evolution of the schema.org Vocabulary over Time Robert Meusel, Christian Bizer and Heiko Paulheim
- 2. 2 Motivation - LODCloud with 1.000 data providers A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary over Time - WIMS 2015
- 3. 3 Motivation - schema.orgMD with 700k data providers A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary over Time - WIMS 2015
- 4. 4 Microdata in aNutshell 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 A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary over Time - WIMS 2015 <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>
- 5. 5 Schema.org in aNutshell 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 A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary over Time - WIMS 2015
- 6. 6 Schema.org in aNutshell – Coverage Schema.org has incorporated some popular vocabularies, like: • Good Relations (2012) • W3C BibExtend (2014) • MusicBrainz vocabulary (2015) • Automotive Ontology (2015) A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary over Time - WIMS 2015
- 7. 7 Microdata with Schema.orgin 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.… A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary over Time - WIMS 2015
- 8. 8 Wrap-Up A Web-scale Studyof the Adoption and Evolution of the schema.org Vocabulary over Time - WIMS 2015 Semantic annotations are used by more and more websites Entities on websites become machine-readable and machine- understandable schema.org together with Microdata is a success story • Promoted by search engine companies • Deployed by over 17% of all websites [1] (over 700k data providers) Usage is more compliant to the schema than e.g. LOD [2] [1] http://webdatacommons.org/structureddata/2014-12/stats/stats.html [2] Meusel and Paulheim, ESWC 2015
- 9. 9 Digging for Reasons So, Microdata is more often deployed and is often more schema compliant, although there are millions of uncontrolled providers with different skill sets But why? Some hypotheses… • Availability of documentation • Tool support • Business incentive • Schema flexibility Can we confirm/reject those from looking at the data? A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary over Time - WIMS 2015
- 10. 10 A Diachronic Perspective Versions of schema.org are archived over time • Plus: there are several crawl releases per year • i.e., we can look at change over time If we look at both schema and deployed data, we may observe • Adoption rates of schema changes • Data-first changes to the schema • Convergence or divergence of deployed data A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary over Time - WIMS 2015
- 11. 11 A Diachronic Perspective Three releases of WDC Microdata corpus [1] • 2012, 2013, and 2014 Versions of schema.org that were valid • At the beginning of the crawl • At the end of the crawl A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary over Time - WIMS 2015 [1] http://webdatacommons.org/structureddata
- 12. 12 Top-down Adoption Howfast are changes in the schema adopted? • New classes/properties • Deprecations • Domain/range changes Measuring adoption: challenges • Different crawls • Overall growth of deployed schema.org Measure: normalized usage increase (nui) from i to j: • nui(s)>1.05: usage of schema element s has increased significantly • nui(s)<0.95: usage of schema element s has decreased significantly A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary over Time - WIMS 2015
- 13. 13 Top-down Adoption Adoptionof new classes and properties • Almost half of all introduced classes are never used! • Similar for new properties Reasons • Bulk-addition of vocabularies • not every term is equally needed • e.g., medical vocabulary • Blind spot of our approach • some terms are mainly for e-mail markup • e.g., Actions A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary over Time - WIMS 2015 SURPRISE!
- 14. 14 Top-down Adoption Maindomains of positive adoption • Meta data for web content (schema.org/Website has the highest nui) • Broadcasting (e.g., TV Episodes) • Questions & Answers • Postal addresses Classes featured in Google Rich Snippets • Still growth on high level (tens of thousands of data providers) • But nui(s)<0.95 A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary over Time - WIMS 2015 Yellow Pages Search Engine Listings Collaboration with BBC and EBU Influence of CMS adoption Q&A Pages, such as Stackoverflow
- 15. 15 Top-down Adoption Adoptionof domain/range changes • Again: rather low overall adoption Adopted well for • Products (height, width, itemCondition, …) • Broadcasting domain (episode, actor, ...) A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary over Time - WIMS 2015 Search Engine Listings Collaboration with BBC and EBU
- 16. 16 Top-down Adoption Adoptionof deprecations • Works well (29 out of 32 have a significantly low nui) Exceptions • s:map (← s:hasMap) • s:maps (← s:hasMap) A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary over Time - WIMS 2015 For Google Maps (lots of outdated tutorials)
- 17. 17 Bottom-up Evolution MartinLuther • Started the protestant church • A success story, too (like schema.org) • (i.e., 800 million adopters worldwide) Famous quote: • “Man muss […] dem gemeinen Mann aufs Maul schauen” • (roughly: “You have to listen to the way the common man really speaks.”) A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary over Time - WIMS 2015 Martin Luther, 1483-1546 Disclaimer: I do not speak for the protestant church.
- 18. 18 Bottom-up Evolution Arenew features in the schema first used “inofficially”? • New classes/properties • Domain/range changes Instrument for measurement: ROC curves • True positives mapped against false positives • tp: elements used before • fp: elements not used before • Ranking by #PLDs A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary over Time - WIMS 2015
- 19. 19 Bottom-up Evolution Thereare some mild influences observable • Stronger for domain/range changes • especially range changes • Weaker for new classes/properties A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary over Time - WIMS 2015 2012→ 2013 2013→ 2014 2012→ 2014 classes properties domains ranges
- 20. 20 Bottom-up Evolution Extensionmechanism • Allows for user-defined classes/properties • Those become subclasses implicitly Analysis over time • No measurable impact on standard evolution • “Inofficial” use is likelier than use of extension mechanism A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary over Time - WIMS 2015 s:Product/ElectronicProduct s:price/reducedPrice
- 21. 21 Overall Convergence Measuringconvergence • i.e., homogeneity of descriptions of classes • Example: two instances of s:LocalBusiness A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary over Time - WIMS 2015 _:1 _:2 “Birmingham” “Main Street 24” s:LocalBusiness s:PostalAddress _:1 _:2 “Liverpool” “Church Street 1” s:LocalBusiness s:PostalAddress
- 22. 22 Overall Convergence Recap •RDF from Microdata is a set of trees • i.e., we can enumerate all paths to leaf nodes (omitting literals) Example: A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary over Time - WIMS 2015 _:1 _:2 “Liverpool” “Church Street 1” s:LocalBusiness s:PostalAddress rdf:type-s:LocalBusiness, s:address-rdf:type-s:PostalAddress, s:address-s:addressLocality, s:address-s:streetAddress
- 23. 23 Overall Convergence Usingall paths, we can compute the entropy for each class as A low entropy refers to a high homogeneity We normalize both by maximum entropy and the total number of paths • i.e., we use normalized entropy rate as a measure for homogeneity A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary over Time - WIMS 2015
- 24. 24 Overall Convergence Observations •Overall entropy decreases over time Classes with high convergence rates • WebSite, Blog, … • Hotel, Restaurant, … • Product, Offer, … • Rating, Review A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary over Time - WIMS 2015 Influence of CMS adoption Yellow pages Google Rich Snippets ...all of the above
- 25. 25 Key Adoption Drivers Search Engine Optimization • Web site providers want to be high in Google rankings • Direct business incentive! Tool adoption • Major CMSs use schema.org Standard Agility • schema.org: 25 revisions in last three years • cf. FOAF: six revisions in last eight years A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary over Time - WIMS 2015
- 26. 26 Summary Both ways,top-down and bottom-up adoptions can be observed Homogeneity of deployed schema increase over time Described empirical data-driven study reveals valuable insights to understand how and why schema.org is a success story Observed key drivers and obstacles can also help to understand and analysis adoption of other standards, e.g. LOD More fine-grained insights might be revealed when extending the analysis corpus to the mailing list archive and issue tracker A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary over Time - WIMS 2015
- 27. 27 Thank you! Questions?Feedback? Raw data can be found on the website of WebDataCommons: http://webdatacommons.org/structureddata/ More interesting datasets and analysis: http://webdatacommons.org/index.html A Web-scale Study of the Adoption and Evolution of the schema.org Vocabulary over Time - WIMS 2015 Acknowledgement The extraction and analysis of the datasets was supported by AWS in Education Grant.