Collective Cognition with Semantic Mediawiki: Lessons and Experiences
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This document discusses lessons learned from using Semantic Mediawiki for collective cognition projects. It identifies common pitfalls like the difficulty of knowledge modeling in RDF triples without training and issues around knowledge organization when facts must be attached to specific pages. Potential solutions proposed include using templates and forms to guide users, extending the syntax to allow facts without corresponding pages, and introducing a context model to represent different points of view similar to Wikipedia's neutral point of view policy.
![Semantic Wiki
• Extensions to Wikis with some Semantic
Web support
– Example: Semantic Mediawiki
Eric Cartman
[[friend of::Butters]] Butters
[[Category:Boy]]
friend of
Cartman
Eric_Cartman friend_of Butters
Eric_Cartman rdf:type Boy
(RDF triple)
7](https://image.slidesharecdn.com/2009-09-17nec-slides-100706160140-phpapp02/85/Collective-Cognition-with-Semantic-Mediawiki-Lessons-and-Experiences-7-320.jpg)
![Organization and Context
Potential Solution
• Extending the SMW syntax
– [[Cartman::friend of::Butters]]
• Introducing a context model to SMW
– Context: Where, Who, When
– No more need to use the subject to locate a triple
16](https://image.slidesharecdn.com/2009-09-17nec-slides-100706160140-phpapp02/85/Collective-Cognition-with-Semantic-Mediawiki-Lessons-and-Experiences-16-320.jpg)
Collective Cognition with Semantic Mediawiki: Lessons and Experiences
- 1. Collective Cognition with Semantic Mediawiki: Lessons and Experiences Jie Bao, Li Ding and James Hendler Tetherless World Constellation, Department of Computer Science Rensselaer Polytechnic Institute, Troy, NY, USA {baojie,dingl,hendler}@cs.rpi.edu Network-Enabled Cognition workshop, ACITA, 2009 Sep 22, 2009, UMUC, Maryland
- 2. Goal • To identify a few common pitfalls and limitations of Semantic Mediawiki in – knowledge modeling, – Knowledge organization and context, and – Collaboration protocols • To examine some potential approaches to solve these problems. 2
- 3. Wiki and Recognition • Wiki is among the most prominent of forms on the Web that harness the distributed, collective efforts of users to create content online. – Ideas are formed, indentified or evolved and – Facts are discovered, refined or removed (for “wrong” ones) In the course of a never-ending editing process. 3
- 4. Collective Cognition • A hypothetical example: write an outline for a new South Park episode. Picture courtesy of Wikipedia 4
- 5. Collective Cognition • 9:00pm: show starts • 9:01pm: Wikipedia page for the episode is created • 9:01-9:08pm: 10 active users are adding summary when they watch • 9:08-9:10pm: ad time, some minor reorganization and typo fixing. • 9:10-9:30pm: repeat the above • 9:30-10:30pm: a user A is doing major refining, e.g. adding culture references • 10:31pm: a user B disagrees with A, undo A’s edit • 10:32pm: A undos B’s edit • 10:33pm: B leaves a message on A’s user page, to avoid further edit wars • 10:43pm: the two agrees to compromise with wording such that “It is implied that Chef is dead at the end of the episode; however, some others believe that it is not the case”. • The next day: user C adds a citation to this South Park episode’s page on a film’s page with that “South Park episode X is a parody of the this film”. 5
- 6. Key elements • Simplicity: least training required to contribute. • AAA: anybody can say anything anywhere • NPOV: neutral point of view (among other collaboration protocols of Wikipedia) 6
- 7. Semantic Wiki • Extensions to Wikis with some Semantic Web support – Example: Semantic Mediawiki Eric Cartman [[friend of::Butters]] Butters [[Category:Boy]] friend of Cartman Eric_Cartman friend_of Butters Eric_Cartman rdf:type Boy (RDF triple) 7
- 8. Semantic Wiki • Fast-growing adoption – Healthcare – E-Government – Entertainment – Consulting – Database – … • Inside ITA – OWL modeling with controlled natural language – Rule modeling 8
- 9. Semantic Wiki • Can Semantic wiki reproduces the success of wiki to be among the most prominent of forms on the Web that harness the distributed, collective efforts of users to create content knowledge online? • We have seen encouraging success in quite a few projects • However, some issues are identified in our real- world experiences. 9
- 10. Knowledge Modeling • Myth: users can do RDF-style (triple- based) modeling on SMW • Fact: few is able to do this (at least without substantial training) 10
- 11. “Big Fat Page” effects We gave a 3 hour training on SMW with a group of undergraduate students (most with no knowledge of RDF), and let them do a collective annotation task on TV shows. However, the result is not fully satisfactory • Difference between categories and properties is not that easy to understand (see a lot misuse like Category:hug) • To describe a thing with triples requires “thinking in RDF”, which needs some experiences. • It is a big headache to choose the right vocabulary and it is hard to know what vocabulary to reuse. As a result, many of the testees simply use the wiki as a notepad, without adding much semantic annotations, resulting in a long single “usual” wiki page. 11
- 12. Schema or not schema? • Two common knowledge models on a semantic wiki, – “Schema”-based modeling, often represented in the form of pre- defined wiki templates, that are used by “common” users of the wiki to access data via forms or prebuilt queries. • c.f. “infobox” in Wikipedia • =>stable, shared knowledge – Arbitrary RDF-style semantic markup - heavily used by a selected few elite group • => less structured, less shared knowledge • A carefully pre-populated wiki “schema” (template), is as important as a schema in a database project. 12
- 13. Template Example Template as Schema Form for the template 13
- 14. Organization and Context • Myth: semantic wiki, like wiki, allows you to write things freely. • Fact: SMW does not support AAA – Every “triple” has to be on its subject’s page. • E.g., “South Park episode X is a parody of the this film” can only be said on X’s page. – Each subject and property of a triple must be a local page name. 14
- 15. Organization and Context Why it may be problematic? • May require the creation of many trivial, small pages. • Is troublesome to describe things (e.g., an external URL) that have no corresponding wiki pages. • Discourages users due to the difficulty of determining where to write knowledge (i.e., the best “subject” pages). • Many users are confused of query-based pages: they do not know how to track the source of the queried results when they want to change a query-based page. 15
- 16. Organization and Context Potential Solution • Extending the SMW syntax – [[Cartman::friend of::Butters]] • Introducing a context model to SMW – Context: Where, Who, When – No more need to use the subject to locate a triple 16
- 17. Collaboration Protocol • Myth: semantic wiki, as wiki always does, allows compromises between different points of view. • Fact: Semantic wiki only allows one version of the (semantic) “truth”. – A triple can not be both true and not true 17
- 18. Ontology War No! Cartman is only a Fictional Character Cartman is a Boy http://www.gambling911.com/files/publisher/cat-fight-032609L.jpg Collaboration Protocol Support Needed! 18
- 19. Collaboration Protocol • Avoid edit wars in Wikipedia – NPOV: allows multiple points of view co-exist on one page verifiable sources. – natural language text can accommodate and explain multiple points of view on a single page 19
- 20. Collaboration Protocol Two possible approaches • To have categories and typed links optionally contextualized by authors, similar to the tag contextualizing mechanism in delicious and flickr. – http://example.com/author/term (contextualized name) – http://example.com/term (non-contextualized name) • To introduce a context model of SMW knowledge statements, so that different versions of truth may be formally represented with explicitly given sources. 20
- 21. Conclusions • Modeling in SMW can be regarded as an evolving cognition process and schema-based modeling is useful. • We showed that a context/provenance model is needed for SMW to support better knowledge organization • Collaboration protocols: to accommodate two versions of a fact, provenance of a term and/or triple should be traceable. 21
- 22. Solution Summary • Simplicity: improve user interaction using forms and templates (schema). • AAA: Context model – On-going work: “Semantic History” • NPOV: Enabled by the context model 22