Your SlideShare is downloading. ×
Tutorial   semantic wikis and applications
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Tutorial semantic wikis and applications

11,317
views

Published on

Tutorial on Semantic Wikis for SemTech 2010

Tutorial on Semantic Wikis for SemTech 2010

Published in: Technology, Education

0 Comments
11 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
11,317
On Slideshare
0
From Embeds
0
Number of Embeds
4
Actions
Shares
0
Downloads
315
Comments
0
Likes
11
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide
  • Namemore limits* what are wikis not good at, like e.g.* implicit knowledge* “why”-questionsGive more questions* what are the research challenges?* what are good ideas for your PhD theses?why do we need semantics?what does semantic even mean here?, i.e. what kind of semantics?A more concise storyline, especially in the second halfWhat do you want to say?
  • Wikis started by adding a simple edit link to a website
  • What a semantic wiki is like
  • The same as the semantic web
  • So why does Wikipedia work, and wiki clock not?
  • How to combine these abilities?
  • Counter information overload with visualizations
  • But getting these visualizations is hard
  • Because then SMW can be used as a database where apps read from it
  • Because then SMW can be used as a database where apps read from it
  • The problem we are going to solve is “find the 0-60 times of all Porsche cars in Wikipedia”This is a sample Wikipedia page for the Porshe 996, showing its acceleration times in a performance data table.This table is manually built – all the table data exists as constants in the table.
  • This is a Wikipedia page showing 0-60 times for the Porsche Cayenne.If we have to manually go through every Porsche model to assemble the 0-60 data for each model and type, this is going to take a while.A better idea is to treat Wikipedia like a database, and simply query it. Enter Ultrapedia.
  • This is the Ultrapedia home page.
  • First notice that Ultrapedia can leverage all the data it extracts from Wikipedia to support a much more helpful UI.For example, Ultrapedia adds a manufacturer-based navigation system on the side, and show explanatory popups. These kinds of UI tweaks aren’t possible with MediaWiki now, and are an important benefit of having the semantic data.
  • Remember that we want to find the 0-60 acceleration data for all Porsche models that Wikipedia knows about.Let’s start by looking at a query generated table on the Ultrapedia Porsche 996 page. For comparison, Ultrapedia also includes the original performance table from Wikipedia (above)
  • This is Ultrapedia’sPorsche 996 performance table, built by a query to the Ultrapedia database of Wikipedia-extracted data.Notice that it has the same information that the original static table has, this is because we scrape the data from the static table.This table is dyamically generated at each page load out of the extracted Wikipedia data, so it is always up to date.It is sortable and also accepts feedback and ratings on individual data items.
  • Now we can answer our question about 0-60 times across all Porsche models with one simple query in Ultrapedia. We can make this an Ultrapedia-only page – the page itself just 5 queries on it (one for each acceleration range).We could also do this as one big table but it’s easier to read as 5 smaller tables.All the data here flows from Wikipedia.
  • Of course once you have data, Ultrapedia can support data visualizations. This is a simple Flash-based chart widget based on the same Porsche 996 data, and included in Ultrapedia’s Porsche 996 page.It shows us that while acceleration varies dramatically, top speed and peak engine power remain fairly constant across models.The chart was specified manually with a query. There are of course a huge number of possible ways to chart a set of data, and most of these ways are uninteresting.In the Ultrapedia concept, we rely on article authors to specify interesting charts for their readers that will support the particular points in the article.
  • We can also use the data to dynamically link to other data sources. In this case we have configured the Ultrapedia Porsche 996 article to include a live ebay query to find out what the Porsche 996 sells for today…We access the ebay data through a web services interface.We can do this for arbitrary other web-service-accessible data sources, like amazon or geonames.In a government or enterprise context, we would link articles to supporting data from appropriate systems of record.
  • I don’t think I’ll be buying one… I think I’d rather send my daughter to college.
  • Pictures automatically get metadata, so Ultrapedia can deliver an iPod-like “cover flow” browsing experience with images to augment the table data. We could also embed images or videos in the tables.
  • Since Ultrapedia includes some simple internal logic about time, we can generate simple browsable timelines and use them in articles.Here we see a timeline of VW models.
  • But, did you know that Uusikaupunki, Finland, is a major hub for Porsche manufacturing?Ultrapedia allows us to drill down to look at Finland’s contribution to Porsche production.
  • Suppose we notice in Ultrapedia that the city of manufacture for the BMW 8-series is not right. Wikipedia (as of our copy) just says “Germany”, so that’s also what Ultrapedia says. But in Ultrapedia we can pop up a data correction dialog, which allows us to comment on this specific piece of data. If we follow the “edit data in Wikipedia” link in the popup, Ultrapedia uses its provenance information to send us to the exact line in Wikipedia where it got the data.
  • Wikis, especially, semantic-enhanced wikis, are wonderful tools for collaboration and content management. Semantic MediaWiki Plus, with Halo and other useful extensions made it a great platform for web application development.
  • With all the semantic structures generated, it is important to empower more people with the magic of this platform. The more people use it, the better it will be.
  • With all the semantic structures generated, it is important to empower more people with the magic of this platform. The more people use it, the better it will be.
  • Microsoft Office application suite has more than 90% market share, generating billions of revenue for Microsoft. Many users are dependent on the application to get their things done, such as Excel, PowerPoint. Outlook, especially, is usually open all the time, and in fact, many people spend most of their work time a day with Outlook. So, if we can entice Microsoft Office users to use Semantic Wiki, it’ll be a great plus. 500 million users is from http://blogs.technet.com/office2010/archive/2009/10/07/new-ways-to-try-and-buy-microsoft-office-2010.aspx
  • WikiTags is here to bridge semantic wikis with more potential users, such as users of Microsoft Word, Outlook and Excel, with Microsoft SmartTag technology.
  • Let's at first take a look at some semantic wikis we have.
  • This is a bare-bone wiki for Sci-Fi movies, similar to Wikipedia except it contains extracted semantic information, shown here in the fact box.
  • Here is another semantic Wiki: a simple form-based proposal tracking application. This sample article is about building a fancy doghouse. You can see the semantic "Facts" too, the cooking ingredients for delicious presentations.
  • We also have a project management and feature documentation wiki , full of semantic templates and forms, so it is also "semanticated“, a wiki of us, for us, and by us.
  • Now, let's see how it works with Office applications.
  • WikiMail let users contribute to the wiki using their familiar tools
  • WikiMail let users contribute to the wiki using their familiar tools
  • WikiMail let users contribute to the wiki using their familiar tools
  • Now, let's see how it works with Office applications.
  • Now you see WikiTags connect multiple wikis to bring relevant info to you when you want it, in your familiar Microsoft Office applications
  • You discover rich and live semantic info, without search; you can further explore the wiki without actually going there.
  • Relevant, context sensitive, semantic actions lead to higher accuracy and productivity; moreover, the semantic action services can also be in the wiki.
  • WikiMail let users contribute to the wiki using their familiar tools
  • WikiMail let users contribute to the wiki using their familiar tools
  • Automatically uploaded and updated articles enable all team in sync with the latest info, and revision history.
  • Power users can have many settings to get the maximum power.
  • WikiTags can help wikis connecting to more people and releasing more power of semantic wikis, and it is available for free trial.
  • Because then SMW can be used as a database where apps read from it
  • Transcript

    • 1. Tutorialon Semantic Wikis and Applications
      Mark Greaves
      Vulcan Inc.
      markg@vulcan.com
      Daniel Hansch
      Ontoprise GmbH
      hansch@ontoprise.de
      Denny Vrandecic
      Karlsruhe Institue of Technology
      Denny.vrandecic@kit.edu
      Jesse Wang
      Vulcan Inc.
      jessew@vulcan.com
    • 2. 2
      Outline
      Tutorial Introduction and Structure (Mark)
      Introduction to Semantic MediaWiki (Denny)
      Dive into Semantic MediaWiki (Denny)
      Applications for Semantic Wikis (Mark)
      Extensions for Semantic MediaWiki (Denny, Daniel, Jesse)
      Connecting Semantic MediaWiki with MS Office (Jesse)
      Augmenting Semantic MediaWiki with a Triple Store (Daniel)
      Future Development (Denny, Daniel, Jesse)
      Wrap Up and Q&A (Mark)
      Break (30 mins)
    • 3. 3
      Outline
      Tutorial Introduction and Structure (Mark)
      Introduction to Semantic MediaWiki (Denny)
      Dive into Semantic MediaWiki (Denny)
      Applications for Semantic Wikis (Mark)
      Extensions for Semantic MediaWiki (Denny, Daniel, Jesse)
      Connecting Semantic MediaWiki with MS Office (Jesse)
      Augmenting Semantic MediaWiki with a Triple Store (Daniel)
      Future Development (Denny, Daniel, Jesse)
      Wrap Up and Q&A (Mark)
      Break (30 mins)
    • 4. Context: Social Web, Semantic Web, and Semantic Wikis
      4
      SoftwareAgents
      Expert Systems
      Freebase
      Schema Integration
      Facebook
      OpenGraph
      Linked Data
      Ontologies
      SemanticWikis
      Semantic
      Desktops
      Evri
      Thesauri
      Twine/T2
      Prediction Markets
      Increasing Data Interconnection
      PIMs
      Ning
      Databases
      FaceBook
      SearchEngines
      Amazon Reviews
      Content Portals
      Web sites
      Wikipedia
      File servers
      Blogs
      Twitter
      Increasing Social Interconnection
      Based on a diagram by Nova Spivak, Radar Networks
    • 5. A Range of Semantic Wiki Platforms
      KiWi – Knowledge in a Wiki
      Knoodl – Semantic Collaboration tool and application platform
      Freebase - Collaborative platform for almanac data by Metaweb
      OntoWiki
      PhpWiki
      Semantic MediaWiki - an extension toMediaWikithat turns it into a semantic wiki (and SMW extensions)
      TikiWiki - CMS/Groupware integrates Semantic links as a core feature
      Wikidsmart - adds semantics to Confluence (from zAgile)
      5
      5
    • 6. 6
      Outline
      Tutorial Introduction and Structure (Mark)
      Introduction to Semantic MediaWiki (Denny)
      Dive into Semantic MediaWiki (Denny)
      Applications for Semantic Wikis (Mark)
      Extensions for Semantic MediaWiki (Denny, Daniel, Jesse)
      Connecting Semantic MediaWiki with MS Office (Jesse)
      Augmenting Semantic MediaWiki with a Triple Store (Daniel)
      Future Development (Denny, Daniel, Jesse)
      Wrap Up and Q&A (Mark)
      Break (30 mins)
    • 7. Semantic MediaWiki
      Denny Vrandečić, KIT / ISI, USC
      June 22 2010, San Francisco
    • 8. Wikis are great
      Enable new scale of human collaboration
      Everyone can read
      Everyone can write
      Everyone gets aggregated
      Everyone is accountable for everything
      But some things are better left to machines…
      8
    • 9. edit
      wow. I can change the web.
      let’s write an encycolpedia!
    • 10.
    • 11. Wiki Clock
      http://pageoftext.com/wikiclock
    • 12. Wikis are great
      Enable new scale of human collaboration
      Everyone can read
      Everyone can write
      Everyone gets aggregated
      Everyone is accountable for everything
      But how are semantic wikis different?
      Semantic
      + computer
      v
      12
    • 13. edit
      edit
      edit
      Country
      City
      Population = 745,514
      Area = 39 km2
      capital
      mayor
      edit
      edit
      Birthdate =
      1 April 1946
    • 14. edit
      edit
      edit
      edit
      edit
      edit
      May 27 1994, Tim Berners-Lee, Keynote at WWW1
    • 15. edit
      edit
    • 16. What humans are good at
      What machines are good at
      Understanding
      “Why”
      Tacit knowledge
      Stories
      Following hunches
      Checking external refs
      Executing
      Facts and figures
      Explicit knowledge
      Keeping track and logs
      Analyzing big style
      Calling web services
    • 17.
    • 18. Universal Access to
      All Knowledge
    • 19. 19
      What Wikipedia knows
      Wikipedia has articles about…
      … all cities
      … their populations
      … their mayors
      So can I ask for a list of the world’s ten largest cities with a female mayor?
    • 20. 20
      Let’s see what happens…
    • 21. Wikipedia’s answer: lists
      21
    • 22.
    • 23.
    • 24.
    • 25.
    • 26. 26
    • 27. 27
    • 28. 28
    • 29. 29
    • 30. 30
    • 31.
    • 32. 32
    • 33.
    • 34. Computers are stupid
      34
    • 35. 35
      What humans see
    • 36. What humans see
      Karlsruhe
      ... has a population of 285,812
      ... is located inGermany
      ... was founded in 1715
      ... has mayor Heinz Fenrich
      36
    • 37. What computers see
    • 38. What computers see
      Karlsruhe
      ... 285,812
      ... Germany
      ... 1715
      ... Heinz Fenrich
      38
    • 39. Computers don‘t make connections
      39
    • 40. Computers need our help
      40
    • 41. Karlsruhe
      Karlsruhe is a city in
      [[Germany]].
      [[Country::Germany]].
      Germany
      Country
      Karlsruhe
      Country
      Germany
      Karlsruhe
      Mayor
      Heinz Fenrich
      Heinz Fenrich
      Gender
      Male
      41
    • 42.
    • 43. {{#ask:
      [[Category:City]]
      [[located in::
      Baden-Württermberg]]
      | format=barchart
      | ?population
      }}
    • 44. External data reuse
      Export formats
      RDF/XML
      SPARQL
      RDFa
      CSV
      JSON
      iCal
      vCard
      Bibtex
      44
    • 45.
    • 46.
    • 47. 47
      Outline
      Tutorial Introduction and Structure (Mark)
      Introduction to Semantic MediaWiki (Denny)
      Dive into Semantic MediaWiki (Denny)
      Applications for Semantic Wikis (Mark)
      Extensions for Semantic MediaWiki (Denny, Daniel, Jesse)
      Connecting Semantic MediaWiki with MS Office (Jesse)
      Augmenting Semantic MediaWiki with a Triple Store (Daniel)
      Future Development (Denny, Daniel, Jesse)
      Wrap Up and Q&A (Mark)
      Break (30 mins)
    • 48. External data reuse
      Computer understands wiki content
      Knowledge based applications
      A number of export formats
      RDF/XML, SPARQL, RDFa, CSV, JSON, iCal, vCard, Bibtex, ...
      RDF APIs in programming languages
      Java, JavaScript, C/C++, Python, Ruby, Haskell, .Net, PHP, Common Lisp, Prolog, …
      Standards based
      URIs, XML, RDF, OWL, SPARQL, …
    • 49. importSemanticMediaWikias smw
      wiki = smw.SMW("http://semanticweb.org/")
      denny = wiki.load(“DennyVrandecic")
      printdenny.affiliation
    • 50. Test wiki
      Go to http://scratchpat.referata.com
      Click on log in and then on “Create an account”
      Suggestion: use your name as your login
      Enter your eMail (for forgotten passwords)
    • 51. Editing the wiki
      Go to your own page (page with your name)
      Click on “edit”
      Try to add or change text
      You can cancel anytime, preview (just for you), or save the changes so that everyone can see them
    • 52. Quick overview of wiki markup
      '''three apostrophes''' will make text bold
      ''two apostrophes''' will make text italic
      [[Text in double square brackets]] will be links to the page named as the text in the brackets
      [[Link target|link text]] will display a link that looks like link textbut links to link target
      The wiki is case sensitve – but not on the first letter of a link
      The wiki is Unicode
    • 53. Slide 53
      Overview of semantic markup
      To add a page P to category C type [[Category:C]] on page P
      To make a typed link of type R from page P1 to page P2 type [[R::P2]] on page P1
      To state the value V of an attribute A on page P type [[A::V]] on page P
      Example:
    • 54. Data values and types
      Attributes like [[birthdate::February 27 1978]] or [[population::3,635,389]] must know the type of the value
      This is done by adding [[has type::T]] on the page of the attribute
      Available, predefined types:
      Telephone number
      Record
      URL
      Email
      Annotation URI
      Geographiccoordinate (S Maps)
      Enumeration
      Customunits
      Page
      String
      Number
      Boolean
      Date
      Text
      Code
      Temperature
    • 55. Add your own information
      Now add information about yourself
      For example: nationality, affiliation, age, birthday, hair color, likes…
      Save or preview to see if and how the information has been understood
      Blue links mean there is a page about it
      Red link means there is no page about it
    • 56. Collaborative ontology engineering
      There are pages describing categories and properties
      Informal description
      Can be discussed
      Can be edited
      Extensional descript.
      List of all instances
      But: only direct ones
      Supercategories
    • 57. Slide 57
      Social aspects
      Task: come up with a vocabulary and the relation between the vocabularies for the whole group, using the wiki
      How to decide which properties and categories are important?
      How to define the properties or categories?
      How to ensure high quality data? What does it mean?
      How to control the wiki knowledge base and its growth?
      Browse the wiki to see the results and connections
    • 58. Querying the knowledge
      Go to Special:Ask
      Enter a query
      Queries look like this:
      Conditions on a category: [[Category:X]]
      Conditions on a property: [[R::X]]
      Property conditions can be ranges, [[R::>X]], [[R::<X]]
      Property conditions: any value [[R::+]]
      Print statements: ?R
      Examples follow
      See also online docs
    • 59. Query examples
      [[population::>1,000,000]] anything with a population of over a Million
      [[located in::Korea]] anything that is located in Korea
      [[affiliation::+]] anything that has any stated affiliation
      [[Category:Tutor]] all tutors
      [[Category:Tutor||Student]] all tutors or students (logical or)
      [[Category:Tutor]] [[Category:Student]] everyone who is both
    • 60. Querying and social aspects
      Querying can only be done on aligned vocabularies
      If half of the people use “affiliation” and the other half “works for” you cannot query the knowledge easily
      Inside SMW, information integration usually happens with social tools, not with technology
      Gardening tools can help with aligning vocabularies, but not replace them
      Tools that allow you to rename a property throughout the wiki
      Or to join two different names
    • 61. Querying the wiki
      {{#ask:
      [[Category:City]]
      [[Mayor.Gender::Female]]
      | sort=Population
      }}
    • 62. Querying the wiki
      {{#ask:
      [[Category:Country]]
      [[Continent::North America]]
      |?Population
      }}
    • 63. Result rendering
    • 64. Querying the wiki
      {{#ask:
      [[Category:Country]]
      [[Continent::North America]]
      |?Population
      |format=piechart
      }}
    • 65. Pie chart
    • 66. Querying the wiki
      {{#ask:
      [[Category:Country]]
      [[Continent::North America]]
      |?Population
      |format=barchart
      }}
    • 67. Bar chart
    • 68. 68
      Outline
      Tutorial Introduction and Structure (Mark)
      Introduction to Semantic MediaWiki (Denny)
      Dive into Semantic MediaWiki (Denny)
      Applications for Semantic Wikis (Mark)
      Extensions for Semantic MediaWiki (Denny, Daniel, Jesse)
      Connecting Semantic MediaWiki with MS Office (Jesse)
      Augmenting Semantic MediaWiki with a Triple Store (Daniel)
      Future Development (Denny, Daniel, Jesse)
      Wrap Up and Q&A (Mark)
      Break (30 mins)
    • 69. SNPedia
    • 70. HL7 Healthcare Terminology Management
      70
    • 71. Taaable
      71
    • 72. Chickipedia
      72
    • 73. Football Indexing Wiki
      • Non-Wikipedia Look/Feel
      • 74. Play-by-play video search
      • 75. Highlight reel generation
      • 76. Search on crowd-defined patterns (“touchdowns with big hits”)
      • 77. Tree-based navigation widget
    • Metacafe Video Indexing and Tagging Wiki
    • 78. SMW+ with Automatic Document Annotation
      75
    • 79. Collaborative Proposal Management at BT with SMW+
      76
      Active Bid Viewer
      Service Desk Selector
    • 80. Lightweight Project Management with SMW+
      77
      • Automatically populate tables
      • 81. Just the data you want, at the level you want
      • 82. Calendars and timelines
      • 83. Workflows
      • 84. Custom Reports
      • 85. Form-oriented inputs
      • 86. Notifications via email/RSS
      • 87. MS Office integration
    • Shared Documentation in SMW+
    • 88. Wiki-based Communities of Interest with SMW+
      79
    • 89. Employee Skill Registries with SMW+
      80
    • 90. Employee Home Pages in SMW+
      81
    • 91. Community Maintained Resource Portals
      82
    • 92. Timelines in the LiDAR Portal
      83
    • 93. Social Networks in the LiDAR Portal
      84
    • 94. Google Maps
    • 95.
    • 96. SMW+ Extended Example: An Analytic Encyclopedia
      Ultrapedia: An SMW demo built to explore data and text in a wiki
      Wikipedia merged with the power of a database
      Data extracted from Wikipedia Infobox and Table data; stored in RDF
      For Authors: tools to create more compelling articles
      Great visualizations: charts, tables, timelines, photos, analytics
      Always up-to-date across the Encyclopedia
      Encourage data consistency and find data errors
      Link in other web data sources
      For Readers:
      Enhanced articles and data interaction
      Faceted navigation
      Sophisticated queries (both standing and ad-hoc)
      Maintenance via the Wikipedia update process
      Data is from the article text, with simple ways for article authors to maintain and extend it.
      Authors and readers always in the loop for merging, updating, validating, mapping
      87
    • 97. Title
      Description
      Languages
      Further Down
      Web Links
      Categorization
      Domain specific
      Data
      Images
      Infobox
      Properties
      Sources of Structured Data in Ultrapedia
    • 98. Ultrapedia Data from Wikipedia Tables
      89
      Table
      Data
    • 99. Ultrapedia: An Analytic Encyclopedia
      Goal: Prototype a small semantic encyclopedia
      Create an semantic version of a part of Wikipedia
      Software is SMW+, Ontobrokertriplestore, DBpedia
      Show what a data-aware encyclopedia might look like
      Ultrapedia Prototype Details
      Test domain is German cars
      ~2500 Wikipedia pages, ~40000 triples
      Features
      Similar look and feel to Wikipedia
      Dynamic tables and charts
      Powerful queries
      Navigation beyond search
      Edit, discuss and rate data
      SPARQL-based queries
      Derived assertions (via OntoBroker)
    • 100. Wikipedia for Porsches (Acceleration Data Example)
      • Information Need: All Porsche models that accelerate 0-100kph in under 5, 6, and 7 seconds
    • More Porsche Acceleration Data in Wikipedia
    • 101. Main Page
      Ultrapedia Main Page
    • 102. Tree View Control
      Abstract/Summary quick preview
      Semantics for Improved Wiki Navigation
    • 103. The Porsche 996 Acceleration Table In Ultrapedia
    • 104. Same Table as a Query
    • 105. Which Porsches accelerate fast?
      Dynamically-Generated Tables for Queries
      • Information Need: All Porsche models that accelerate 0-100kph in under 5, 6, and 7 seconds
    • Graph Views of the Acceleration Data
    • 106. External Data via a Live Ebay Query
    • 107. Linking to External Ebay Data
    • 108. Mercedes-Benz E-class W212 Gallery Section
      Photos in Wiki Articles as Data
    • 109. Volkswagen Production Timeline View
      Timelines from Data
    • 110. Dynamic Mapping and Charting
    • 111. Editing Wiki Data In Place
    • 112. 105
      Wrap-Up Part 1: Managing Data in the 21st Century
      A New Kind of Knowledge Management
      Structured and unstructured data together in one tool
      Built with Semantic Web standards, and with web energy
      Empower users with lightweight, web-friendly tools
      Data sharing from the start: not just another silo
      Built for collaboration on the web
      Example: Semantic MediaWikiand SMW+
      Open-source semantic wiki software
      Wiki-style text/article authorship based on MediaWiki
      Lightweight enterprise-scale data publishing
      Collaborative, user-governed structured and unstructured data curation
      A comfortable tool users to own their data
      A variety of applications and uses
    • 113. 106
      Break
    • 114. 107
      Outline
      Tutorial Introduction and Structure (Mark)
      Introduction to Semantic MediaWiki (Denny)
      Dive into Semantic MediaWiki (Denny)
      Applications for Semantic Wikis (Mark)
      Extensions for Semantic MediaWiki (Denny, Daniel, Jesse)
      Connecting Semantic MediaWiki with MS Office (Jesse)
      Augmenting Semantic MediaWiki with a Triple Store (Daniel)
      Future Development (Denny, Daniel, Jesse)
      Wrap Up and Q&A (Mark)
      Break (30 mins)
    • 115. Extensions
      Halo
      S Forms
      S Result Formats
      S Layers
      S Tasks
      S Calendar
      MokiWiki
      External Data
      Maps and S Maps
      S Drilldown
      Woogle
      Innsbruck Ontology Editor
      P2P Extension
      RDFaExporter
      and others
    • 116. Extension architecture
      Built firmly on top of MediaWiki
      Core SMW to be small
      Provide extension hooks of its own
      Allow apps on top of it
    • 117. The suite of halo extensions for Semantic MediaWiki
      Daniel Hansch, „Semantic Wikis and Applications“ tutorial, semtech 2010
      hansch@ontoprise.de
    • 118. Agenda
      Who is ontoprise?
      The halo extensions
      Vision
      Benefits
      How to get it?
    • 119. ontoprise: leader in semantic technologies and solutions
      • +50 employees in Karlsruhe, Germany
      • 120. software vendor (more about us on: http://www.ontoprise.com)
      • 121. flagship product: rdf-store with reasoning capabilities (OntoBroker)
      • 122. selected customers
      • 123. selected partners
    • Building the best Semantic Wiki in project halo[1]
      SMW+
      halo extensions
      1. http://wiki.ontoprise.com/wiki/index.php/Faq/project_halo
    • 124. halo extensions - vision
      Leverage adoption of Semantic MediaWiki by
      domain experts in scientific and commercial environments
      by improving key product features.
      Usability
      Retrieval
      Security
      SemanticMediaWiki
      Data
      processing
      Data re-use
      and
      consistency
      Administration
    • 125. Improve usability
      Enable a non-tech savvy Wiki community to efficiently use
      Wiki- and semantic features with minimal training time.
    • 126. Improve usability
      Ontology browser
    • 127. Improve usability
      Ontology browser
      Enable a non-tech savvy Wiki community to efficiently use
      Wiki- and semantic features with minimal training time.
    • 128. Improve usability
      Graphical query interface
    • 129. Improve usability
      Graphical query interface
      Ontology browser
      Enable a non-tech savvy Wiki community to efficiently use
      Wiki- and semantic features with minimal training time.
    • 130. Improve usability
      Annotation mode
    • 131. Improve usability
      Graphical query interface
      Annotation mode
      Ontology browser
      Enable a non-tech savvy Wiki community to efficiently use
      Wiki- and semantic features with minimal training time.
    • 132. Improve usability
      WYSIWYG editor
    • 133. Improve usability
      Graphical query interface
      Annotation mode
      Ontology browser
      WYSIWYG editor
      Enable a non-tech savvy Wiki community to efficiently use
      Wiki- and semantic features with minimal training time.
    • 134. Get better search results
      Augmented search results
      Path search
      Semantic tree view
      SMW blends text and data; this requires augmenting classic retrieval
      and navigation features with semantic data.
    • 135. Enforce security policies for text and data
      Protection of content and data
      Protection of annotations and queries
      User group management
      Commercial environments require integration with central directory
      services and fine grained access rights to semantic data and content.
    • 136. Leverage data re-use and improve consistency
      2. Select available webservices
      Import legacy data and tab data from web services to embed the Wiki
      into a team’s data-environment. Data inconsistencies are automatically
      detected to improve data quality.
      3. Embedding webservices in articles
      1. Attach webservices using GUI
    • 137. Powerful data processing
      Enhanced data model
      Professional Wiki communities request the ability to formulate complex
      relationships in the Wiki (e.g. rules), which are processed automatically.
      Form based rule editor
    • 138. Reduced administration overhead
      Reduce the efforts for checking for compatible upgrades to a
      SMW installation and for downloading and installing new
      extensions.
    • 139. Where to get it?
      Get a copy: http://sourceforge.net/projects/halo-extension/
      User forum: http://smwforum.ontoprise.com
      It‘s all for free and GPL!*
      *) Except OntoBroker and Triple store connector which are ontoprise licenses.
    • 140. too complicated?
      then get all these features within 5 minutes:
      Product home page: http://wiki.ontoprise.com
    • 141. Semantic Forms
      Utilizing the semantics
      Facilitating data input
    • 142. Benefits of Semantic Forms
      To make MediaWiki templatesbetter to use
      To provide a form-like User Interfaces for inexperienced users to input data
      To associate forms with a category
      Have a helper form to help wiki admins or advanced userscreate forms
      Variations to provide further usability enhancements
    • 143. Image: Using a (basic) Form
    • 144. Image: Using a (long) form
    • 145. Image: Form with a (simple) style
      http://www.thethirdturn.com/w/index.php?title=Form:Driver&action=edit
    • 146. Image: Form with auto-completion
      Advanced Auto-Completion on Customized Query Results
      Basic Auto-Completion on Static Permitted Values
    • 147. Remember Special Properties?
      “Has type” is a special property
      a pre-defined property for meta-data
      Example: [[Has type::Type:Date]]
      “Allows value” is another special property
      To specify the permitted values for the property
      Example:
      [[Allows value::Low]]
      [[Allows value::Medium]]
      [[Allows value::High]]
    • 148. Form Field Input Types
      String, Page, Number – text entry
      Text – TextArea
      Boolean – checkbox
      Date – date input or Javascriptdatepicker
      “Enumeration” (Page or String with “allowed values”) - DropDown list or RadioButton
      List of "Enumerations" - ListBoxor CheckBoxes
    • 149. Creating Forms: on Form Page
    • 150. More On Auto-Completion
      Basic auto-completion is on “Allowed values”
      Current standard is on either category or property
      Advanced auto-completion is based on queries
      {{{field|story|autocomplete on query=[[Category:Project stories]]
      [[Project sprint::<q>
      [[Sprint start date::<{{CURRENTYEAR}}/{{CURRENTMONTH}}/{{CURRENTDAY}}]]
      [[Sprint end date::>{{CURRENTYEAR}}/{{CURRENTMONTH}}/{{CURRENTDAY}}]]
      </q>]]}}}
    • 151. Helper Forms
    • 152. Helper Form : Create a Template
    • 153. Helper Form: Create a Form
    • 154. Helper Form: Create a Class
    • 155. Special Pages:
      Special:CreateForm - lets a user create a new form for adding/editing data. (See example of page)
      Special:CreateTemplate - lets a user create a new template. (See example of page)
      Special:CreateProperty - lets a user create a new property. (See example of page)
      Special:CreateCategory - lets a user create a new category. (See example of page)
      Special:CreateClass - a page that creates all the elements for a single "class" at the same time - properties, template, form and category (See example of page). Access to this page is dictated by the 'createclass' MediaWiki permission; by default, it is available to all logged-in users.
      Special:FormEdit - lets a user either create or edit a page using a user-defined form. (See example of page.) (This page was, until version 1.9, two separate pages: "Special:AddData" and "Special:EditData".)
      Special:FormStart - used to route a user to either 'FormEdit' or the relevant page's "edit with form" tab. This page should not be accessed directly by users. (This page was known until version 1.9 as "Special:AddPage".)
      Special:Forms - lists all form pages on the site. (See example of page)
      Special:RunQuery - lets a user run a query, using a form (See example of page)
      Special:Templates - lists all templates on the site. (See example of page)
      Special:UploadWindow - lets a user upload a file; very similar to the standard Special:Upload page, but without the skin. This page is called from within a form, and should not be accessed directly by users.
    • 156. More Info
      On MediaWiki.org
      http://www.mediawiki.org/wiki/Extension:Semantic_Forms
      SMW User Forum (ontoprise GmbH) http://smwforum.ontoprise.com/smwforum/index.php?title=Help:Creating_Semantic_Forms&context=Help%3ASMW%2B+1.5.0
    • 157. 147
      Outline
      Tutorial Introduction and Structure (Mark)
      Introduction to Semantic MediaWiki (Denny)
      Dive into Semantic MediaWiki (Denny)
      Applications for Semantic Wikis (Mark)
      Extensions for Semantic MediaWiki (Denny, Daniel, Jesse)
      Connecting Semantic MediaWiki with MS Office (Jesse)
      Augmenting Semantic MediaWiki with a Triple Store (Daniel)
      Future Development (Denny, Daniel, Jesse)
      Wrap Up and Q&A (Mark)
      Break (30 mins)
    • 158. SMW:: powerful tools and contents
      Semantic MediaWiki and related extensions have more potential power
    • 159. Need Release ::The (more) Power
      Be used by morepeople
      Content in moreplaces
      Accessible via moreapplications
      Enhanced with moresemantics
      The more users
      The better
    • 160. Need ::Workflow Integration + Usability Enhancements
      InfrequentWiki users frequentlyforget where the wiki pages are located
      Search is a break from current workflow
      Search result can be noisyorirrelevant
      Usability:
      Wiki/Template/SF markup syntax is not extremely hard, but enough to turn off many users
      To locate and consume info in SMW is just not easy enough, need something better
      Why don’t we leverage Microsoft Office suite?
    • 161. Microsoft Office ::The Most Popular Productivity Suite
    • 162. WikiTags:: How It Works
      Leverage Microsoft SmartTags technology
      Bring SMW info to Office applications on-demand
      API for semantic data I/O
      Utilize semantics to improve relevance
      Smart actions for semantic properties
      SmartTag
      Add-ins
      API
      API
      Connections
      Smarts
    • 163. Some Semantic Wikis
      Before the demo, let’s look at
      For more info, go to
      http://wiking.vulcan.com/dev/
    • 164. Wiki:: Semantic Sci-Fi Movie
      Familiar content just like another wiki
      Semantic markups shown in fact box
    • 165. Wiki:: NGT - Proposal Handling
    • 166. Wiki :: Agile Project Management
      • Project Wiki for Milestones, User Stories, Developer Tasks, etc.
      • 167. Page is form- based, with queries and semantics built-in
    • Live actions
      Now see the demo
      For more info, go to
      http://wiking.vulcan.com/dev/
    • 168. Backstage::WikiTags Extension
      Wiki Validation
      Authentication
      To get the categories
      And descriptions
      To get the article titles
      To get the semantic properties
      To get page info
      Get all available forms
      Save page as a form
      Save page with dataset
      Set form of a page
      Create form templates
      To upload into the Wiki
      http://wiking.vulcan.com/dev/index.php/SMW_Webservice_APIs
    • 169. Extension to facilitate semantic data exchange
      Web UI to make semantic schema mapping for semantic wiki templates and forms
      Web service APIs to do the same
      http://wiking.vulcan.com/dev/index.php/SemanticConnector_extension
      Backstage::Semantic Connector
    • 170. Special:ApiTest :: Results in JSON
    • 171. Recap of demo
      What to take away from the demo
      For more info, go to
      http://wiking.vulcan.com/wikitags/
    • 172. Semantic Info::Across Office Apps
      Dynamic Query Results from the article page
      Outlook
      Multiple Wiki Sites supported
      Excel
      Via SmartTags
      WikiTags recognizes smartly the keywords or phrases relevant to you
    • 173. Semantic Info::In Real-time
      Explore related real-time semantic info across the links in article
      See articles in categories live
    • 174. WikiTags::Semantic Actions
      Semantic actions are based on semantic services of properties
    • 175. Wiki Forms::In Microsoft Office
      View Semantic Content in familiar forms
      Contribute into Wiki articles back
    • 176. Screenshots::WikiMail
      • Upload Emails into Wiki
      • 177. Manual upload
      • 178. Automatic upload by folder mapping
      • 179. Conflict handling
      • 180. Heuristic category
      • 181. Use recipients, folders and text
      Shortcut to create wiki articles
      Customize Categories
      Conflict Resolution
    • 182. Screenshot::Email in Wiki
      Attachments automatically uploaded too
    • 183. Screenshots::Settings
      Option to choose categories of interest
      and more
    • 184. WikiTags::Smart Connections
      • Consume relevant, targeted information
      • 185. With the tools you are already familiar with
      • 186. In the context – better relevance and productivity
      • 187. Only when you need it – no information overload
      • 188. In place – no search overhead to break workflow
      • 189. In real time – data from wiki is live
      • 190. Let you contribute to Wiki
      • 191. Without knowing where the content is
      • 192. Without learning wiki/template syntax
    • 170
      Outline
      Tutorial Introduction and Structure (Mark)
      Introduction to Semantic MediaWiki (Denny)
      Dive into Semantic MediaWiki (Denny)
      Applications for Semantic Wikis (Mark)
      Extensions for Semantic MediaWiki (Denny, Daniel, Jesse)
      Connecting Semantic MediaWiki with MS Office (Jesse)
      Augmenting Semantic MediaWiki with a Triple Store (Daniel)
      Future Development (Denny, Daniel, Jesse)
      Wrap Up and Q&A (Mark)
      Break (30 mins)
    • 193. Making SMW smarter - Agenda
      RDF and Semantic MediaWiki (SMW)
      Current limitations of SMW
      What is a triple store?
      What is the Triple Store Connector?
      Examples
      Derived properties
      Semantic data integration (demo!)
      Where to get it?
      Wrap up
    • 194. RDF and Semantic MediaWiki
      RDF (Resource Description Framework) is the underlying data model of the Semantic Web and essentially also for SMW.
      RDF is a graph-base datamodel, i.e. all data is represented in the form or nodes that are connected via directed, labeled arcs. Two nodes connected by a arc should be interpreted as a subject-predicate-object statement (triple).
      Examples of three triples.
      SMW stores such triples in the underlying relational database
    • 195. Current limitations of SMW
      SMW is a great semantic Web application, in the sweet spot between feature richness and engineering complexity.
      Limitations:
      Triple stores overcome these limitations.
    • 196. What is a triple store?
      A triple store is a dedicated database for storing and retrieving RDF data.
      Features:
      Ontoprise’s “Triple store connector" creates a bridge between SMW and a triple store.
    • 197. What is the Triple Store Connector?
      Triple Store Connector
      Semantic MediaWiki
      halo Extension
      Triple Store
      The Triple Store Connector[1] is a ready-to-use product from ontoprise which is installed along with SMW and attaches it to Jena[2] or OntoBroker[3].
      [1] http://smwforum.ontoprise.com/smwforum/index.php/Triple_store_connector
      [2] Open source triple store with reasoning capabilities, http://www.openjena.org/
      [3] Highly scalable semantic Web middleware, http://www.ontoprise.de/en/home/products/ontobroker/
    • 198. Examples
      Derived properties
      We formulate a rule in the Wiki to derive a property value from other properties (e.g. calculation).
      Advantage: reduced amount of annotations, improved data consistency and enriched knowledge.
      Semantic data integration
      An enterprise integrates sources of legacy data into one single source and publishes this data in the Wiki.
      Advantage: data from rigid legacy systems is available for highly collaborative and flexible workflows.
    • 199. First example – derived properties
      We have a Wiki which is used for generating bids; the project team wants to calculate the estimated costs of each task from the estimated work efforts which are given in person days.
      Want to try out this example by yourself?
      Go to our online demo installation and create an account:
      http://smwdemo.ontoprise.com
    • 200. Preparing the ontology
    • 201. Preparing the ontology
    • 202. Formulating the calculation rule
    • 203. Formulating the calculation rule
    • 204. Formulating the calculation rule
    • 205. Applying the rule
    • 206. Applying the rule
    • 207. Applying the rule
    • 208. Benefits
      We learn from this example:
      Authoring rules in a triple store connector-backed Wiki is making it a powerful data processing tool.
    • 209. Second example: semantic data integration
      Large corporations have to deal with data silos making integrated views onto data hard to achieve.
      Resulting problems:
      We require a Wiki which is giving access to semantically integrated legacy data.
    • 210. Architecture (draft)
      Ontology engineering application (OntoStudio)
      Semantic MediaWiki
      Triple Store Connector
      OntoBroker
      Web
      services
      RDBMS
      RDBMS
      RDBMS
      RDBMS
    • 211. Demonstration: Workflow
      We want to provide a Wiki community with legacy data about book titles[1], the community queries the data in the Wiki and enriches it with socially curated metadata.
      Steps:
      Integrate relational data: the knowledge manager uses the ontology engineering tool „OntoStudio“ to attach the RDBMS to OntoBroker and to generate the ontology integrating the data about book titles into the Wiki.
      Query the integrated data: the user queries the Wiki for the book titles to generate a personalized views which can be embedded into articles.
      Curate the integrated data: the user tags (“annotates”) individual book titles with new meta data which can be used in queries again.
      [1] http://msdn.microsoft.com/en-us/library/aa238305(SQL.80).aspx
    • 212. Demonstration
    • 213. Benefits from integrating legacy data into the Wiki
      We learn from this example:
      Data from rigid legacy systems are available in highly collaborative and flexible workflows.
    • 214. Where to get it?
      Option 1: You are a beginner and just want to give it a try:
      • study examples online in the smwforum: ref. [1]
      • 215. try them out in the online demo system: ref. [2]
      Option 2: You want to make your SMW installation smarter:
      • get the halo extension for free: ref. [3]
      • 216. get the Triple store connector basic for free: ref. [4]
      Option 3: You require a smart Wiki for your team or department:
      • get SMW+, the Semantic Enterprise Wiki: ref. [5]
      • 217. get the Triple store connector basic for free: ref. [4]
      Option 4: You to go enterprise level?
      • get SMW+, the Semantic Enterprise Wiki: ref. [5]
      • 218. get the Triple store connector professional: shop@ontoprise.com
      [1] http://smwforum.ontoprise.com/smwforum/index.php?title=Help:Creating_rules&context=Help%3ARule+Knowledge+Extension+1.1.0
      [2]http://smwdemo.ontoprise.com
      [3]http://smwforum.ontoprise.com/smwforum/index.php/Help:Halo_Extension_User_Manual
      [4]http://smwforum.ontoprise.com/smwforum/index.php/Help:Installing_the_Basic_Triplestore_1.2_with_Installer
      [5]http://smwforum.ontoprise.com/smwforum/index.php/Download
    • 219. Take home message
      • Semantic MediaWiki is a great tool! But it has limitations with regard to the query language, data processing and data integration capabilities.
      • 220. SMW can be improved by further semantic extensions, such as the HALO extensions.
      • 221. SMW becomes versatile and smarter by using a triple store with reasoning capabilities, e.g. for reasoning and semantic data integration*
      • 222. Interconnect an entry-level triple store or an enterprise-level triple store to SMW with ontoprise‘s Triple store connector.
      • 223. Read more here: http://wiki.ontoprise.com
      *) With OntoBroker.
    • 224. 194
      Outline
      Tutorial Introduction and Structure (Mark)
      Introduction to Semantic MediaWiki (Denny)
      Dive into Semantic MediaWiki (Denny)
      Applications for Semantic Wikis (Mark)
      Extensions for Semantic MediaWiki (Denny, Daniel, Jesse)
      Connecting Semantic MediaWiki with MS Office (Jesse)
      Augmenting Semantic MediaWiki with a Triple Store (Daniel)
      Future Development (Denny, Daniel, Jesse)
      Wrap Up and Q&A (Mark)
      Break (30 mins)
    • 225.
    • 226.
    • 227.
    • 228.
    • 229.
    • 230. Better Wiki I/O
      Better workflow integration
      On-demand client UI using wiki data
      Smarter WikiTags matching (IR tricks)
      Subversion and other tools integration
      Multi-model authentication support (NTLM etc.)
      Automatic and more powerful forms
      For more info, visit http://wiking.vulcan.com/dev/Wiking
    • 231. SMW+ and the halo extensions
      We make SMW a citizen of the Web of Data
      import (or remote query) and map linked data sources in the Wiki
      use data in queries
      publish data
      Jesse Wang & Daniel Hansch - SemTech 2010
    • 232. SMW+ and the halo extensions
      Usability improvements
      Renovated graphical query interface
      Faceted browsing
      Notifications on semantic data
      Jesse Wang & Daniel Hansch - SemTech 2010
    • 233. SMW+ and the halo extensions
      Easier knowledge formulation
      Tabular forms
      Easier semantic forms
      Generating forms automatically
      ..and much much more!
      Jesse Wang & Daniel Hansch - SemTech 2010
    • 234. 204
      Thank You
      Questions?