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2/24(Wed) - PowerPoint Presentation
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2/24(Wed) - PowerPoint Presentation
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2/24(Wed) - PowerPoint Presentation
2/24(Wed) - PowerPoint Presentation
2/24(Wed) - PowerPoint Presentation
2/24(Wed) - PowerPoint Presentation
2/24(Wed) - PowerPoint Presentation
2/24(Wed) - PowerPoint Presentation
2/24(Wed) - PowerPoint Presentation
2/24(Wed) - PowerPoint Presentation
2/24(Wed) - PowerPoint Presentation
2/24(Wed) - PowerPoint Presentation
2/24(Wed) - PowerPoint Presentation
2/24(Wed) - PowerPoint Presentation
2/24(Wed) - PowerPoint Presentation
2/24(Wed) - PowerPoint Presentation
2/24(Wed) - PowerPoint Presentation
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2/24(Wed) - PowerPoint Presentation
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2/24(Wed) - PowerPoint Presentation
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2/24(Wed) - PowerPoint Presentation
2/24(Wed) - PowerPoint Presentation
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2/24(Wed) - PowerPoint Presentation
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2/24(Wed) - PowerPoint Presentation
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2/24(Wed) - PowerPoint Presentation
2/24(Wed) - PowerPoint Presentation
2/24(Wed) - PowerPoint Presentation
2/24(Wed) - PowerPoint Presentation
2/24(Wed) - PowerPoint Presentation
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  • 1. Mobile Ontology Cloud- Semantic Post-IT-<br />IT Life and Ontology<br />Key-Sun Choi (kschoi@kaist.edu)<br />http://kschoi.kaist.ac.kr/ <br />CILab & Semantic Web Research<br />
  • 2. 1st day: what we will learn<br />)<br /><ul><li>What is Semantic Post-it? (15 min)
  • 3. Demo and Downloadable (5 min)
  • 4. Enabling Technologies (15 min)
  • 5. APIs for Technologies (5 min)
  • 6. ontocore.org (what you can do),
  • 7. Protégé API
  • 8. Remaining in your home
  • 9. References to read and to use </li></li></ul><li>What is Semantic Post-it?: Contents<br /><ul><li>As Mobile App
  • 10. Personal Ontology Editors
  • 11. Benefits when interpreting the input messages</li></li></ul><li>Introduction<br />What is the Semantic Post-It?<br /><ul><li>A system that maps personal randomized message into well-organized personal information space based on collective intelligence.
  • 12. Personal randomized message
  • 13. Organizing by interpreting messages
  • 14. Table information extraction from text
  • 15. Relevant table information grouping
  • 16. Personal information space
  • 17. Usage of ontology that user can edit
  • 18. Collective intelligence
  • 19. Usage of pivot ontology based on Wikipedia (web-based encyclopedia that anyone can edit) </li></li></ul><li>Windows Mobile<br />isDevelopedBy<br />Microsoft<br />Windows Mobile<br />isDevelopedBy<br />Microsoft<br />Omnia 2<br />ISA<br />smartphone<br />Omnia 2<br />ISA<br />smartphone<br />Flash memory <br />ISA<br />computer storage <br />Flash memory <br />ISA<br />computer storage <br />Omnia 2<br />hasOS<br />Windows Mobile<br />Omnia 2<br />hasOS<br />Windows Mobile<br />Omnia 2<br />hasMemory<br />Flash memory<br />Omnia 2<br />hasMemory<br />Flash memory<br />Introduction<br />A working flow of Semantic Post-It<br />Omnia 2 is a multimedia smartphone announced at Samsung. Omnia 2 runs Windows Mobile and comes with flash memory..<br />Contents Space<br />Flash memory is a non-volatile computer storage that <br />can be electrically erased and reprogrammed.<br />Windows Mobile is a compact mobile operating system developed by Microsoft<br />Omnia 2 is a multimedia smartphone announced at Samsung. Omnia 2 runs Windows Mobile and comes with flash memory..<br />Message Space<br />Triple Message Space<br />(Table information)<br />hasMemory<br />hasOS<br />Linked Triple Message Space<br />
  • 20. Motivation<br />Motivating Scenario<br />Reading an article on “Omnia 2”<br />Another similar Smartphone? <br />More details on OS<br />What is the recent trend of it?<br />Company in competition<br />What should we do?<br />
  • 21. Motivation<br />Motivating Scenario<br />CPU clock<br />Reading an article on “Omnia 2”<br />Another similar Smartphone? <br />OS, platform<br />More details on OS<br />What is the recent trend of it?<br />Manufacturer, design<br />Company in competition<br />Products of the company<br />We have to think of what type of information are involved<br />
  • 22. Motivation<br />Motivating Scenario<br />nationality<br />Reading an article on “Immanuel Kant”<br />Where is he from?<br />?<br />?<br />?<br />If new to philosophers, <br />we are likely to have no idea about relevant information<br />
  • 23. Motivation<br />What is the solution?<br /><ul><li>We need a system that retrieves relevant information
  • 24. Data set that specifies attributes for each concepts is needed
  • 25. Smartphone : manufacturer, OS, memory, …
  • 26. Philosophers : nationality, follower, teacher, …
  • 27. However, no one guy can describe every concepts
  • 28. We can obtain the data set from collective intelligence</li></ul>author<br />politician<br />scientist<br />engineer<br />Philosophers<br />Artist<br />
  • 29. Established<br />February 16, 1971<br />Type<br />Government-run<br />President<br />Nam-Pyo Suh<br />…<br />…<br />Motivation<br />Wikipedia<br />Wikipedia documents (2010/01/29)<br />3,175,836 (ENG) - 11,527,437 users<br />125,801 (KOR) - 100,498 users<br />④<br />①<br />②<br />① Inter-page link<br />② Inter-Language link<br />③ Category<br />④ Infobox: table information<br />③<br />
  • 30. Background Technologies<br />New paradigm<br /><ul><li>A few years have passed since a new paradigm was introduced.
  • 31. Semantic Web
  • 32. A machine-readable web
  • 33. Ontology
  • 34. A formal specification of knowledge</li></li></ul><li>Background Technologies<br />Semantic Web<br /><ul><li>An evolving development of the World Wide Web
  • 35. The meaning (semantics) of information and services on the web is defined
  • 36. For the web to "understand" and satisfy the requests of people and machines to use the web content</li></ul>Our focus<br />Adapted from Wikipedia<br />(http://en.wikipedia.org/wiki/Semantic_Web)<br />
  • 37. Background Technologies<br />RDF<br /><ul><li>Resource Description Framework</li></ul>A Wikipedia article about Tony Benn<br /><http://en.wikipedia.org/wiki/Tony_Benn> <http://purl.org/dc/elements/1.1/title> "Tony Benn" .<br /><http://en.wikipedia.org/wiki/Tony_Benn> <http://purl.org/dc/elements/1.1/publisher> "Wikipedia" .<br /><rdf:RDF<br />xmlns:rdf=http://www.w3.org/1999/02/22-rdf-syntax-ns#<br />xmlns:dc=http://purl.org/dc/elements/1.1/><br /><rdf:Descriptionrdf:about=http://en.wikipedia.org/wiki/Tony_Benn><br /><dc:title>Tony Benn</dc:title><br /><dc:publisher>Wikipedia</dc:publisher><br /></rdf:Description><br /></rdf:RDF><br />An expression of “triple”<br />Adapted from Wikipedia<br />(http://en.wikipedia.org/wiki/Resource_Description_Framework)<br />
  • 38. Background Technologies<br />Ontology<br />A formal specification of knowledge to be interpreted by computers<br />Company<br />isManufacturedBy<br />supportSoftware<br />OS<br />releaseDate<br />rdfs:subPropertyOf<br />runsOn<br />cameraPixelOf<br />Mobile Phone<br />hasMemorySize<br />supportOnlineSoftware<br />hasWebsite<br />Mobile PhoneSoftware<br />rdfs:subClassOf<br />rdfs:subClassOf<br />rdfs:subClassOf<br />PDA<br />Cellular<br />Phone<br />SmartPhone<br />Cellular<br />Phone<br />Smart Phone<br />PDA<br />Schema<br />Instance<br />releaseDate<br />2008<br />Samsung<br />i900 Omnia<br />cameraPixelOf<br />5 megapixels<br />supportOnlineSoftware<br />hasWebsite<br />Skype<br />www.skype.com<br />hasMemorySize<br />128 MB<br />runsOn<br />Windows<br />Mobile 6.1<br />isManufacturedBy<br />Samsung<br />
  • 39. Illustrative Example<br />Content Space -> Message Space<br />Semantic Post-It<br />(Message List)<br />Typical Web Browser<br />External Contents<br />KAIST is located in Daejeon, South Korea. KAIST was established by Korean government in 1971 <br />Scrap<br />Related Problems : Mash-Up<br />How to extract text from heterogeneous contents (in a context, not a scientific issue)<br />
  • 40. Illustrative Example<br />Message Space -> Triple Message Space (1/2)<br />Semantic Post-It<br />(Message List)<br />Semantic Post-It<br />(Detail View)<br />Semantic Post-It<br />(Detail View)<br />KAIST is located in Daejeon, South Korea. KAIST was established by Korean government in 1971 <br />KAIST is located in Daejeon, South Korea. KAIST was established by Korean government in 1971.… The current KAIST President Nam Pyo Suhtaught for…<br />KAIST is located in Daejeon, South Korea. KAIST was established by Korean government in 1971.… The current KAIST President Nam Pyo Suhtaught for…<br />Person<br />Smatphone’s UI is limited. Information should be shown by one-click.<br />Related Problems : ISA relation recognition<br />
  • 41. Estabilshed<br />1971<br />Province<br />Daejeon<br />Country<br />South Korea<br />…<br />…<br />Illustrative Example<br />Message Space -> Triple Message Space(2/2)<br />Semantic Post-It<br />(Table View)<br />Semantic Post-It<br />(Message List)<br />Semantic Post-It<br />(Message View)<br />KAIST is located in Daejeon, South Korea. KAIST was established by Korean government in 1971 <br />KAIST is located in Daejeon, South Korea. KAIST was established by Korean government in 1971.… The current KAIST President Nam Pyo Suhtaught for…<br />KAIST<br />Summarization<br />Display size is too small to do full browsing.<br />Related Problems : Triple extraction from text<br />
  • 42. Illustrative Example<br />Triple Message Space ->Linked Triple Message Space<br />Semantic Post-It<br />(Graph View)<br />Semantic Post-It<br />(Message List)<br />Semantic Post-It<br />(Message View)<br />Suh was born in Korea on April 22, 1936, and immigrated to the U.S. in 1954….<br />KAIST is located in Daejeon, South Korea. KAIST was established by Korean government in 1971 <br />KAIST is located in Daejeon, South Korea. KAIST was established by Korean government in 1971.… The current KAIST President Nam Pyo Suhtaught for…<br />president<br />KAIST is located in Daedeok…<br />province<br />Daejeon is a center of transportation in South Korea, where two major,<br />Relevant messages<br />Display size is too small to show text<br />Related Problems : Relevant keyword search by traversing Ontology<br />
  • 43. Illustrative Example<br />Linked Triple Message Space<br />Semantic Post-It<br />(Using Ontology 1)<br />Semantic Post-It<br />(Using Ontology 2)<br />Ontology 1<br />Ontology 2<br />Suh was born in Korea on April 22, 1936, and immigrated to the U.S. in 1954….<br />Suh was born in Korea on April 22, 1936, and immigrated to the U.S. in 1954….<br />University<br />University<br />president<br />president<br />president<br />president<br />KAIST is located in Daedeok…<br />KAIST is located in Daedeok…<br />Person<br />Person<br />province<br />province<br />province<br />locatedAt<br />Settlement<br />Country<br />Daejeon is a center of transportation in South Korea, where two major,<br />South Korea is a presidential republic consisting of 16 administrative…<br />Related Problems : Personal ontology editing, logical consistency checking<br />
  • 44. Illustrative Example<br />Personal Ontology Editor<br /><ul><li>Rename the property name
  • 45. If you wish to see another label in the link
  • 46. Ex) isManufacturedBy -> manufacturer
  • 47. Modify constraints
  • 48. If you wish to see the country name rather than the city name
  • 49. Ex)
  • 50. Remove : University-province-Settlement
  • 51. Add : University-locatedAt-Country
  • 52. Use the modified ontology in your Semantic Post-It</li></ul>How to embed this complex UI into Smartphone?<br />http://protege.stanford.edu/<br />
  • 53. System architecture (1/2)<br />Message Interpretation Services<br />HTTP request<br />Semantic Post-IT Server<br />(HTTP server)<br />Semantic Post-IT client<br />TABLEGEN<br />CAT2ISA<br />HTTP response<br />Ontology Access<br />DBpedia Access<br />Personal Ontology<br />Local Message DB<br />External Message Service<br />System Message DB<br />Twitter, Blog, Email, Calendar, …<br />
  • 54. System architecture (2/2)<br />Semantic Post-IT client<br /><ul><li>Local Message DB controller
  • 55. Message input interface
  • 56. Message list viewer
  • 57. HTTP service controller
  • 58. Semantic Post-IT server
  • 59. External message service
  • 60. Message relation graph viewer
  • 61. Personal ontology editor</li></ul>Semantic Post-IT client<br />Personal Ontology<br />Local Message DB<br />
  • 62. Demo and Downloadable<br /> http://swrc.kaist.ac.kr/SemanticToolkits/<br />
  • 63. What is Semantic Post-It?<br />Memo Admin Service <br />Evernote, quickies, etc.<br />Semantic Service Mash-Up<br />
  • 64. Semantic Service Mash-up<br />Definition of 3 types of applications<br />Type 1 Application: Information zooming on specific ‘word’ of a memo<br />Type 2 Application: Memo Contents Analysis<br />Type 3 Application: Information zooming on whole context of a memo<br />
  • 65. Type 1 Application: Example<br />DEMO: Semantic Post-It<br />
  • 66. Type 2 Application: Demo<br />DEMO: Semantic Post-It<br />
  • 67. Type 3 Application: Demo<br />DEMO: Semantic Post-It<br />
  • 68. Structure of Semantic Post-It<br />Post-It Server<br />Post-It Client<br />Service<br />Repository<br />Communication between<br />Server and Client<br />Provide application List<br />Application Install <br />Request for new application<br />Execute<br />application<br />Request<br />Ontology<br />Ontology Request Module<br />Enterprise Part:<br />Add-on of <br />Semantic Applications<br />Shared Memo Request Module<br />Return shared memos which the client have requested<br />Can download shared memo to local database<br />Request<br />Shared Memo<br />Add new memo<br />Delete memo<br />Find<br />Related <br />Memo<br />Change memo<br />Synchronization Module<br /><ul><li>Synchronization between Server & Client</li></ul>Tag memo<br />Synchroni-zation<br />Attach ontology to memo<br />Shared <br />Memo<br />Ontology<br />Repository<br />Local File System<br />Personal<br />Memos<br />Wikipedia Documents<br />PURE PART<br />
  • 69. Support for Semantic Post-It:OntoCloud<br />Ontology derived from Wikipedia infoboxes<br />Official Website:http://swrc.kaist.ac.kr/ontocloud/<br />
  • 70. Support for Type 2 Application:Semantic Annotation<br />One of possible type 2 application: Table-form summary generator<br />Semantic Annotation: Mark on the documents – ‘which part’ could be transformed into table?<br />
  • 71. Semantic Annotation Toolkit: COAT<br />DEMO: COAT<br />
  • 72. From annotated data to Application: Machine Learning Feature<br />Support Vector Machine(SVM)<br />
  • 73. Ontology Feature<br /> Modern GSM-based BlackBerry handhelds incorporate an ARM 7 or 9 processor, while older BlackBerry 950 and 957 handheldsused Intel 80386 processors.<br />IT Ontology Package<br />Gathering semantic Info<br />Using Ontology<br />CPU<br />Intel 80386<br /> Modern GSM-based BlackBerry handhelds incorporate an ARM 7 or 9 processor, while older BlackBerry 950 and 957 handheldsused Intel 80386 processors.<br />useCPU<br />
  • 74. Data Authority Policy<br />Annotators can check his/her documents ONLY!<br />To prevent cheating<br />Simple annotation data viewer is available<br />For administrators<br />DEMO: COAT Viewer<br />
  • 75. Support for Type 3 Application:300M Wikipedia articles into Database<br />Provide baseline for shared memo<br />For type 3 application<br />Build shared memo database with 300M wikipedia articles as its part<br />
  • 76. Screenshots<br />3) Table information extraction<br />1) User inputs message<br />2) Ontology recommendation<br />4) Relevant message grouping<br />
  • 77. Enabling Technologies<br /><ul><li>CAT2ISA
  • 78. Table Generator  </li></li></ul><li>Technologies<br />Ontology expression<br />OWL (Web Ontology Language)<br /><owl:Class rdf:ID=“Mobile Phone"/> <br /><owl:Class rdf:ID=“PDA"><br /><rdfs:subClassOf rdf:resource=“# Mobile Phone"/><br /></owl:Class><br /><owl:Class rdf:ID=“SmartPhone"><br /><rdfs:subClassOf rdf:resource="# Mobile Phone"/><br /></owl:Class><br /><owl:Class rdf:ID=“Cellular Phone"><br /><rdfs:subClassOf rdf:resource="# Mobile Phone"/><br /></owl:Class><br /><owl:Class rdf:ID=“Mobile Phone Software"/><br /><owl:ObjectProperty rdf:ID=“hasSoftware"><br /><rdfs:domain rdf:resource="#Mobile Phone”/><br /><rdfs:range rdf:resource=“# Mobile Phone Software"/><br /></owl:ObjectProperty><br /><owl:ObjectProperty rdf:ID=“hasOnlineSoftware"><br /><rdfs:subPropertyOf rdf:resource=“#hasSoftware"/><br /></owl:ObjectProperty><br />
  • 79. Technologies<br />Ontology inference<br />Text<br />Text<br />Samsung releases Omnia<br />Text<br />Apple releases <br />iPhone<br />IPTV service is launched<br />Environmental<br />Technology<br />Apple supports Green technologies<br />support<br />ISA<br />beginService<br />Service<br />TV Service<br />Company<br />instanceOf<br />instanceOf<br />instanceOf<br />manufacture<br />use<br />instanceOf<br />Samsung<br />Apple<br />Product<br />instanceOf<br />beginService<br />Green<br />Technology<br />IPTV<br />support<br />manufacture<br />manufacture<br />ISA<br />ISA<br />HDTV<br />Device<br />Omnia<br />Software<br />ISA<br />instanceOf<br />iPhone<br />Smartphone<br />instanceOf<br />
  • 80. Technologies<br />Ontology construction from Wikipedia Infobox<br />class<br />instance<br />properties<br />university<br />
  • 81. Technologies<br />Ontology construction from text<br />2. Taxonomy Construction<br />is-a<br />3. Relation Addition<br />not is-a<br />1. Term extraction and conceptualization<br />The other<br />Final Ontology<br />Existing Ontology<br />Part-of<br />equipment-of<br />4. Integration<br />5. Verification<br />Part-of<br />equipment-of<br />
  • 82. Technologies<br />COAT (CoreOnto Annotation Toolkit)<br /><ul><li>Term and relation annotation</li></li></ul><li>Technologies<br />Ontology construction cost reduction<br />Improve Ontology extension tech. and automation<br />Web-scale annotation by ontology extension tech.<br />2<br />Ontology extension cost reduction by automation<br />1<br />Devise ontology extension tech.<br />Cost reduction<br /><ul><li>Manual annotation cost reduction by using COAT
  • 83. Further reduction could be possible if we can automate the process</li></ul>COAT<br />Auto<br />COAT<br />Before COAT<br />
  • 84. CAT2ISA (cdh4696@world.kaist)<br /><ul><li>Technology for expanding semantic infrastructure
  • 85. Extract semantic information from anonymous category system</li></li></ul><li>CAT2ISA<br /><ul><li>Extract isa/instanceOf relation
  • 86. A instanceOf B: A is a member of set B
  • 87. A is called 'instance', B is called 'concept'
  • 88. A and B must share 'essential properties': Properties that makes something as itself
  • 89. Example: <Key-Sun Choi, instanceOf, Professor>: X<Key-Sun Choi, instanceOf, Human>: O
  • 90. B isa C: B is a subset of C</li></ul> <br /><ul><li>isa/instanceOf relation: vital component in many semantic applications(e.g. semantic search, Q&A system, etc.)</li></ul> <br /> <br />
  • 91. Table Generator (cdh)<br /><ul><li>Summarize a text into table format based on its semantic tag</li></li></ul><li>Table Generator<br /><ul><li>Information extraction using "Ontology"
  • 92. Ontology: Formal representation of a set of concepts within a domain and the relationships between those concepts
  • 93. Ontology-based information extraction:</li></li></ul><li>Remaining for your home: references<br /><ul><li>History of Word Wide Web
  • 94. Berners-Lee, Tim; Fischetti, Mark (1999). Weaving the Web. HarperSanFrancisco.
  • 95. The Semantic Web
  • 96. Berners-Lee, Tim; James Hendler and Ora Lassila (May 17, 2001). "The Semantic Web". Scientific American Magazine.
  • 97. Grigoris Antoniou, Frank van Harmelen (March 31, 2008). A Semantic Web Primer, 2nd Edition
  • 98. Ontology
  • 99. Dean Allemang, James Hendler (May 9, 2008). Semantic Web for the Working Ontologist: Effective Modeling in RDFS and OWL. Morgan Kaufmann</li></li></ul><li>Remaining for your home: Use experiences<br /><ul><li>[1] P. Mistry, P. Maes. Quickies: Intelligent Sticky Notes. In the Proceedings of 4th International Conference on Intelligent Environments (IE08). Seattle, USA. 2008
  • 100. [2] Max Van Kleek, Michael Bernstein, Katrina Panovich, Greg Vargas, David Karger, and mc schraefel, Note-to-Self: Examining Personal Information Keeping in a Lightweight Note-Taking Tool.. CHI, 2009
  • 101. [3] The Tabulator, http://www.w3.org/2005/ajar/tab
  • 102. Read [1,2,3] and use the system [2,3]
  • 103. Try also the following system
  • 104. http://www.evernote.com/
  • 105. Smartphone version is available</li></li></ul><li>2nd day<br /><ul><li>Deep story about semantic technology (20 min)
  • 106. Wikipedia DbpediaOntocloud (kekeeo@world.kaist.ac.kr)
  • 107. What are the upside?
  • 108. Email 3.0
  • 109. Information Zooming
  • 110. Mobile hyperlink
  • 111. Personal Preference Ontology and its use
  • 112. Collective semantic intelligence of LOD + ontology cloud
  • 113. Another demo (5 min)
  • 114. What you can do immediately (review)
  • 115. What you can contribute (review)
  • 116. Big picture
  • 117. Function, society
  • 118. Technology to study </li></li></ul><li>IT-Life Ontology<br />
  • 119. IT Campus Domain Ontology (Partial)<br />
  • 120. Wikipedia (http://en.wikipedia.org)<br /><ul><li>What is Wikipedia?
  • 121. An online, collaboratively edited encyclopedia
  • 122. Articles are available in over 250 languages
  • 123. Freely available and freely distributable
  • 124. Inter-language (interwiki) page links</li></li></ul><li>DBpedia (http://dbpedia.org)<br /><ul><li>What is the DBpedia?
  • 125. A community effort to extract structured information from Wikipedia
  • 126. Available on the Web
  • 127. Different types of structured information 
  • 128. Infobox templates: summaries of the most relevant facts contained in an article
  • 129. Categorization information
  • 130. Images
  • 131. Geo-coordinates
  • 132. Links to external Web pages </li></li></ul><li>OntoCloud<br /><ul><li>Our own constructed Ontology
  • 133. Goals 
  • 134. Making more intelligent IT systems focusing on devices and resources
  • 135. Key classes
  • 136. Device, Product, Resource, Technology, Person and Company</li></li></ul><li>Structure of OntoCloud<br /><ul><li> Template Ontology
  • 137. Constructing the Pivot dataset
  • 138. The infobox dataset from DBpedia3.4 (semi-automated)
  • 139. IT CUO (IT Core Upper Ontology)
  • 140. A middle level ontology for integration
  • 141. Ontologies under IT domains
  • 142. IT Service Ontology
  • 143. IT Device Ontology
  • 144. IT Core Ontology </li></li></ul><li>Mobile 3.0 and its Requirements (full picture: jha)<br /><ul><li>Email 3.0
  • 145. Information Zooming
  • 146. Mobile hyperlink
  • 147. Personal Preference Ontology and its use
  • 148. Collective semantic intelligence of LOD + ontology cloud</li></li></ul><li>E-mail 3.0(email categorization)<br />Automatically map into a class in ontology<br />Related Problems<br /><ul><li>Topic detection</li></ul>Current Status<br /><ul><li>Categorization of long and well-formed text (e.g. Wikipedia documents)</li></ul>Challenges<br /><ul><li>Short message interpretation
  • 149. Personal writing styles</li></li></ul><li>E-mail 3.0(Recipient recommendation)<br />Automatically recommend person to whom the message should be sent<br />sender@abc.com<br />Challenges<br /><ul><li>Task Ontology modeling</li></li></ul><li>E-mail 3.0(Relevant information attachment)<br />Automatically attach pictures<br />sender@abc.com<br />Automatically attach files in local disk<br />Challenges<br /><ul><li>Semantic tags on multimedia data
  • 150. Local file indexing</li></ul>The Samsung Group is composed of numerous international affiliated businesses, most of them united under the Samsung brand including Samsung Electronics, the world's largest electronics company, <br />
  • 151. Topic<br />Information retrieval<br />Deadline<br />Mar 30, 2010<br />Organizer<br />Benno Stein<br />E-mail 3.0(Mash-up Services)<br />A message in inbox<br />Automatically create to-do list<br />The following list organizes classic and ongoing topics from the field<br />of text-based IR for which contributions are welcome:<br />- Theory. Retrieval models, language models, similarity measures,<br />formal analysis<br />- Mining and Classification. Category formation, clustering, entity<br />resolution, document classification <br />---------------------------------------------------------------------------<br />Important Dates:<br />---------------------------------------------------------------------------<br />Mar 30, 2010 Deadline for paper submission<br />Apr 20, 2010 Notification to authors<br />May 17, 2010 Camera-ready copy due<br />Aug 30, 2010 Workshop opens<br />---------------------------------------------------------------------------<br />Workshop Organization:<br />---------------------------------------------------------------------------<br />Benno Stein, Bauhaus University Weimar<br />Michael Granitzer, Know-Center Graz & Graz University of Technology<br />Contact: tir@webis.de<br />Information about the workshop can be found at http://www.tir.webis.de<br />Related Problems:<br /><ul><li>Table information extraction
  • 152. Mash-up</li></ul>Current Status<br /><ul><li>Table information generation from text</li></ul>Challenges<br /><ul><li>Table information generation from semi-structured text</li></li></ul><li>Information Zooming<br /><ul><li> What is information zooming?
  • 153. Show small amount of information first
  • 154. When user requires more information about one part, shows more detailed information about that part.
  • 155. Why is it necessary?
  • 156. Mobile environment: small display
  • 157. We cannot show all the necessary information at once! (Lack of space)</li></li></ul><li>Information Zooming in Semantic Post-It<br /><ul><li> Information zooming for one word</li></ul> <br /> <br /> <br /> <br /> <br /> <br /><ul><li>Information zooming for whole memo</li></li></ul><li>Mobile hyperlink<br /><ul><li> What is mobile hyperlink?
  • 158. Represent URL as barcode
  • 159. Take a picture of the barcode using camera in cellphone and you move to that URL!
  • 160.  Why is it necessary?
  • 161. Mobile environment: small interface
  • 162. Hard to type all the URL
  • 163. Example of mobile hyperlink</li></ul> <br /> <br /><ul><li>QR code:</li></ul>                            <br />
  • 164. Personal Preference Ontology and its use<br /><ul><li>Task of packaging from a potentially large ontology, one or several significant sub-parts
  • 165. Knowledge sharing and re-use crucial research issues</li></ul> <br /><ul><li>On-demand Extraction Service
  • 166. Takes a concept and extract the relations
  • 167.  
  • 168. Interactive Service
  • 169. The user have to select class and relations to consider</li></li></ul><li>Collective semantic intelligence of LOD + ontology cloud<br />
  • 170. The Linked Open Data Cloud<br />
  • 171. What you can do immediately<br />review<br />discussion<br />
  • 172. What you can contribute<br /><ul><li>Data Synchronization for Mobile applications
  • 173. Synchronization is a data transfer between computer and mobile device that aims to keep both of components in a coherent state
  • 174. Knowledge-driven Security Handling for Mobile Applications
  • 175. Several mobile applications attacks have beenrecently reported
  • 176. Device  and environment
  • 177. Ontology Packaging for Mobile field
  • 178. Bacause of its physical aspect, a mobile device has a limited processing and computing capabilities </li></li></ul><li>Big picture<br /><ul><li>Function, society and Technology to study </li></li></ul><li>Windows Mobile<br />isDevelopedBy<br />Microsoft<br />Windows Mobile<br />isDevelopedBy<br />Microsoft<br />Omnia 2<br />ISA<br />smartphone<br />Omnia 2<br />ISA<br />smartphone<br />Flash memory <br />ISA<br />computer storage <br />Flash memory <br />ISA<br />computer storage <br />Omnia 2<br />hasOS<br />Windows Mobile<br />Omnia 2<br />hasOS<br />Windows Mobile<br />Omnia 2<br />hasMemory<br />Flash memory<br />Omnia 2<br />hasMemory<br />Flash memory<br />Big Picture<br />A working flow of Semantic Post-It<br />Contents Space<br />Omnia 2 is a multimedia smartphone announced at Samsung. Omnia 2 runs Windows Mobile and comes with flash memory..<br />Flash memory is a non-volatile computer storage that <br />can be electrically erased and reprogrammed.<br />Windows Mobile is a compact mobile operating system developed by Microsoft<br />Message Space<br />Omnia 2 is a multimedia smartphone announced at Samsung. Omnia 2 runs Windows Mobile and comes with flash memory..<br />Triple Message Space<br />(Table information)<br />Linked Triple Message Space<br />hasMemory<br />hasOS<br />What is the next step?<br />
  • 179. Windows Mobile<br />isDevelopedBy<br />Microsoft<br />Omnia 2<br />ISA<br />smartphone<br />Flash memory <br />ISA<br />computer storage <br />Omnia 2<br />hasOS<br />Windows Mobile<br />Omnia 2<br />hasMemory<br />Flash memory<br />Big Picture<br />Message generation<br />Linked Triple Message Space<br />hasMemory<br />hasOS<br />Personalized Message Space<br />Omnia 2 is a multimedia smartphone announced at Samsung. Omnia 2 runs Windows Mobile developed by Microsoft and comes with flash memory which is a computer storage.<br />How to do so? <br />Do you have an idea how to utilize personalized ontology to generate sentences?<br />Personalized ontology<br />
  • 180. Established<br />1971<br />Province<br />Daejeon<br />Country<br />South Korea<br />…<br />…<br />Big Picture<br />Functions (1/2)<br /><ul><li>From text to presentation file
  • 181. Challenges
  • 182. Semantic Tagging to Image
  • 183. Refer to http://www.image-net.org/</li></ul>KAIST is located in Daejeon, South Korea. KAIST was established by Korean government in 1971 <br />KAIST<br />established<br />1971<br />province<br />Daejeon<br />Country<br />South Korea<br />Table information<br />Table information + images<br />
  • 184. Big Picture<br />Functions (2/2)<br /><ul><li>From table to text
  • 185. Generate NL text by traversing table
  • 186. KAIST-Province-Daejeon
  • 187. Daejeon-Districts-fifth
  • 188.  KAIST is located in Daejeon. Daejeon is the fifth largest city in the country.
  • 189. Challenges
  • 190. Transform a predicate into verb phrases
  • 191. Ex) Province -> is located in</li></li></ul><li>Big Picture<br />Society<br />Message Interpretation Services<br />HTTP request<br />Semantic Post-IT Server<br />(HTTP server)<br />Semantic Post-IT client<br />TABLEGEN<br />CAT2ISA<br />HTTP response<br />Personal Ontology<br />Ontology Access<br />DBpedia Access<br />Local Message DB<br />OpenAPI generator<br />Make your own message interpretation modules and upload it.<br />OpenAPI generator will make it available as an OpenAPI service.<br />
  • 192. Big Picture<br />Technologies to study(interdisciplinary)<br />Architecture<br />/Urban design<br />Software Engineering<br />Graphics<br />IR, AI, MachineLearning<br />HCI<br />CognitiveScience<br />Internet of Things<br />Cloud Computing<br />Design<br />DB & <br />Data Mining<br />ConvergenceNetworks<br />Sociology<br />Middleware<br />Handle huge amount of messages<br />Ex) manipulating Wikipedia documents<br />Find person of my interests<br />Ex) References in papers<br />Which layout is suitable for the display?<br />Ex) Table-memo for a tiny display<br />Plug-in architecture<br />Ex) Collect personal documents by using Google Desktop APIs<br />How to extract table data from memo?<br />Ex) information extraction from document<br />Which type of memo? Writing style anaylsys<br />Ex) To-do list, contact, documents<br />Write message anywhere and anytime<br />Ex) RFID-equipped notes<br />
  • 193. Deep story about semantic technology<br />discussion!<br />
  • 194. Credits<br />Dong-Hyun Choi, cdh4696@world.kaist.ac.kr<br />Eun-Kyung Kim, kekeeo@world.kaist.ac.kr<br />JinhyunAhn, jhahn@world.kaist.ac.kr<br />Key-Sun Choi, kschoi@kaist.edu<br />http://swrc.kaist.ac.kr/ontocloud<br />http://swrc.kaist.ac.kr/SemanticToolkits/<br />

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