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|lastname=Baso
|lastname=Baso
|tags=Schema.org, Machine Learning, Templates, Wikidata, Structured Data, Wikibase, Mobile
|tags=Schema.org, Machine Learning, Templates, Wikidata, Structured Data, Wikibase, Mobile
|primarysession=Session:1
|primarysession=Session:10
|secondarysessions=Structured Data, Applying Machine Learning and Artificial Intelligence
|secondarysessions=Session:6;Session:1
|statement=== Structure Most Things with Schema.org ==
|statement=== Structure Most Things with Schema.org ==



Latest revision as of 11:09, 14 December 2017

Tags Machine Learning, Mobile, Schema.org, Structured Data, Templates, Wikibase, Wikidata
Primary Session Knowledge as a Service
Secondary Sessions Research, Analytics, and Machine Learning, Advancing the Contributor Experience

Structure Most Things with Schema.org

The future of digital information will likely be brokered by major platform providers such as Google, Apple, Amazon, Microsoft, and international equivalents and social networks. We're thankful they extend our reach, even as we seek to help consumers on the platforms join our movement.

We could help platform providers, their users, and our users solve problems better through adoption of the open standard Schema.org into Wikipedia pages mapped with templates and, ideally, federated and synchronized Wikidata properties.

Benefits:

  1. Wikipedia will have even better presentation and placement in search engines and other data rich experiences.
  2. We provide an opportunity for a more consistent data model for template authors and people/bots filling template values. And the richly defined Schema.org entities provide a good target to reach on all entities represented in the Wikipedia/Wikimedia corpora. Standardization can reduce duplication of effort and inconsistencies.
  3. We introduce an easier vector for mobile contribution, which could include simpler and different data entry, mapping, and modeling.
  4. We can elevate an open standard and push its adoption forward while increasing the movement's standing in the open standards community.
  5. Schema.org compliant data is more easily amenable to machine learning models that cover data structures, the relations between entities, and the dynamics of sociotechnical systems. This could bolster practical applications like vandalism detection, coverage analysis, and much more.
  6. This might provide a means for the education sector to educate students about knowledge creation, and data modeling, and more. It might also afford scientists and other practitioners a further standardized way to model the knowledge in their fields.

What would it take? And can this be done in harmony with the existing {{Template}} system?

This session will discuss the following:

  1. Are we aligned on the benefits, and which ones?
  2. Implementation options.
    1. Can we extend templates so they could be mapped to Schema.org?
      1. Would it be okay to derive the mapping by manual and automated analysis at WMF/WMDE and apply it behind the scenes? Would that be sustainable?
      2. Could we make it easy for template authors to mark up their templates for Schema.org compatibility and have some level of enforcement? Could Schema.org attributes and entity types be autosuggested for template creators?
    2. Is it easy to relate the most existing and proposed Wikidata entity types and properties to existing Schema.org entities and properties?
    3. What would it take to streamline MCR Schema.org data structures or MCR Wikibase property clusters mapped to Schema.org on defined entity types?
    4. Furthermore, if we can do #1 and #2, what's to prevent us from letting templates as is merely be the interface for Schema.org compliant Wikibase entities and properties (e.g., by duck typing / autosynthesis)?
    5. How could we bidirectionally synchronized between Wikipedia and Wikidata with confidence in a way compatible with patroller expectations? And what storage and event processing would be needed? Can the systems be scaled in a way to accommodate arrival of real-time and increasingly fine grained information?