This document outlines an agenda for a tutorial on semantic wikis and applications. The tutorial will include introductions to Semantic MediaWiki, diving deeper into its features, applications of semantic wikis, extensions for Semantic MediaWiki developed by various contributors, connecting Semantic MediaWiki with MS Office, augmenting it with a triple store, discussing future development, and concluding with a question and answer session, followed by a 30 minute break.
From text to entities: Information Extraction in the Era of Knowledge GraphsGraphRM
Incontro del 23/07/2018
In recent years there has been a proliferation of free and commercial "knowledge graphs" (KGs), which represent real-world entities together with their semantic relationships in a graphical form. Those are becoming a powerful asset both for tech giants, with Google Knowledge Graph, IBM’s Watson QA system and Facebook’s Open Graph, as well as for startups that are developing AI products, such as, semantic search, data analytics, recommender systems. While KGs provide a structured access to a large amount of knowledge, a vast majority of the information available on the Web is still inaccessible because encoded only in the form of natural-language text. The talk will present an overview of public available KGs and the main techniques used to bridge unstructured text with them, enabling a wide variety of knowledge-based applications.
Speaker: Matteo Cannaviccio
DBpedia Spotlight: a configurable annotation tool to support a variety of use cases. Given input text in English, we extract DBpedia Resources and generate annotations according to user-provided configuration parameters. These parameters can include score thresholds, entity types, and even arbitrary "type" definitions through SPARQL queries.
This is the presentation at the best paper award session at I-SEMANTICS 2011.
DBpedia Spotlight is a tool employed in the Extraction stage of the LOD Lyfe Cycle, performing Entity Recognition and Linking. Although the tool currently specializes in English language, the support for other languages is currently being tested, and demos for German, Dutch and others are available or underway. The tool can be used to enable faceted browsing, semantic search, among other applications. In this webinar we will describe what is DBpedia Spotlight, how it works and how can you benefit from it in your application.
If you are interested in Linked (Open) Data principles and mechanisms, LOD tools & services and concrete use cases that can be realised using LOD then join us in the free LOD2 webinar series!
http://lod2.eu/BlogPost/webinar-series
Entity Search: The Last Decade and the Nextkrisztianbalog
Keynote talk given at the 10th Russian Summer School in Information Retrieval (RuSSIR ’16), Saratov, Russia, August 2016.
Note: part of the work is under still review; those slides are not yet included.
From Exploratory Search to Web Search and back - PIKM 2010Roku
The power of search is with no doubt one of the main aspects for the success of the Web. Currently available search engines on the Web allow to return results with a high precision. Nevertheless, if we limit our attention only to lookup search we are missing another important search task. In exploratory search, the user is willing not only to find documents relevant with respect to her query but she is also interested in learning, discovering and understanding novel knowledge on complex and sometimes unknown topics.
In the paper we address this issue presenting LED, a web based system that aims to improve (lookup) Web search by enabling users to properly explore knowledge associated to her query. We rely on DBpedia to explore the semantics of keywords within the query thus suggesting potentially interesting related topics/keywords to the user.
From text to entities: Information Extraction in the Era of Knowledge GraphsGraphRM
Incontro del 23/07/2018
In recent years there has been a proliferation of free and commercial "knowledge graphs" (KGs), which represent real-world entities together with their semantic relationships in a graphical form. Those are becoming a powerful asset both for tech giants, with Google Knowledge Graph, IBM’s Watson QA system and Facebook’s Open Graph, as well as for startups that are developing AI products, such as, semantic search, data analytics, recommender systems. While KGs provide a structured access to a large amount of knowledge, a vast majority of the information available on the Web is still inaccessible because encoded only in the form of natural-language text. The talk will present an overview of public available KGs and the main techniques used to bridge unstructured text with them, enabling a wide variety of knowledge-based applications.
Speaker: Matteo Cannaviccio
DBpedia Spotlight: a configurable annotation tool to support a variety of use cases. Given input text in English, we extract DBpedia Resources and generate annotations according to user-provided configuration parameters. These parameters can include score thresholds, entity types, and even arbitrary "type" definitions through SPARQL queries.
This is the presentation at the best paper award session at I-SEMANTICS 2011.
DBpedia Spotlight is a tool employed in the Extraction stage of the LOD Lyfe Cycle, performing Entity Recognition and Linking. Although the tool currently specializes in English language, the support for other languages is currently being tested, and demos for German, Dutch and others are available or underway. The tool can be used to enable faceted browsing, semantic search, among other applications. In this webinar we will describe what is DBpedia Spotlight, how it works and how can you benefit from it in your application.
If you are interested in Linked (Open) Data principles and mechanisms, LOD tools & services and concrete use cases that can be realised using LOD then join us in the free LOD2 webinar series!
http://lod2.eu/BlogPost/webinar-series
Entity Search: The Last Decade and the Nextkrisztianbalog
Keynote talk given at the 10th Russian Summer School in Information Retrieval (RuSSIR ’16), Saratov, Russia, August 2016.
Note: part of the work is under still review; those slides are not yet included.
From Exploratory Search to Web Search and back - PIKM 2010Roku
The power of search is with no doubt one of the main aspects for the success of the Web. Currently available search engines on the Web allow to return results with a high precision. Nevertheless, if we limit our attention only to lookup search we are missing another important search task. In exploratory search, the user is willing not only to find documents relevant with respect to her query but she is also interested in learning, discovering and understanding novel knowledge on complex and sometimes unknown topics.
In the paper we address this issue presenting LED, a web based system that aims to improve (lookup) Web search by enabling users to properly explore knowledge associated to her query. We rely on DBpedia to explore the semantics of keywords within the query thus suggesting potentially interesting related topics/keywords to the user.
Presentation about Semantic MediaWiki and Semantic Forms given by Sergey Chernyshev and Yaron Koren at "Semantic Wikis" (March 2008 NY SemWeb Meetup) on March 13, 2008
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1ZW7TDL.
Richard Dallaway shows an example of what Scala looks like when using pattern matching over classes, how to encode an idea into types and use advanced features of Scala without complicating the code. Filmed at qconlondon.com.
Richard Dallaway is a partner at Underscore -- a consultancy specializing in Scala, especially the type-driven and functional aspects of Scala. He works on client projects writing software and helping teams deliver software with Scala. His focus is on the web, machine learning, and code review. He's the co-author of "Essential Slick" (Underscore), and author of the "Lift Cookbook" (O'Reilly).
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This is my invited talk on Semantic Wiki to the Key Lab of Intelligent Information Processing at Fudan University in Shanghai during ASWC 2009 when I gave a similar tutorial on semantic mediawiki and applications.
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The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
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Tutorial semantic wikis and applications
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)
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!
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
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
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?
41. Karlsruhe Karlsruhe is a city in [[Germany]]. [[Country::Germany]]. Germany Country Karlsruhe Country Germany Karlsruhe Mayor Heinz Fenrich Heinz Fenrich Gender Male 41
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, …
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 }}
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)
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
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)
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
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?
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.
127. Improve usability Ontology browser Enable a non-tech savvy Wiki community to efficiently use Wiki- and semantic features with minimal training time.
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.
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.
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
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
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
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>]]}}}
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?
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
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
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
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
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
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.
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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
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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)
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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
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