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2/24(Wed) - PowerPoint Presentation 2/24(Wed) - PowerPoint Presentation Presentation Transcript

  • Mobile Ontology Cloud- Semantic Post-IT-
    IT Life and Ontology
    Key-Sun Choi (kschoi@kaist.edu)
    http://kschoi.kaist.ac.kr/
    CILab & Semantic Web Research
  • 1st day: what we will learn
    )
    • What is Semantic Post-it? (15 min)
    • Demo and Downloadable (5 min)
    • Enabling Technologies (15 min)
    • APIs for Technologies (5 min)
    • ontocore.org (what you can do),
    • Protégé API
    • Remaining in your home
    • References to read and to use
  • What is Semantic Post-it?: Contents
    • As Mobile App
    • Personal Ontology Editors
    • Benefits when interpreting the input messages
  • Introduction
    What is the Semantic Post-It?
    • A system that maps personal randomized message into well-organized personal information space based on collective intelligence.
    • Personal randomized message
    • Organizing by interpreting messages
    • Table information extraction from text
    • Relevant table information grouping
    • Personal information space
    • Usage of ontology that user can edit
    • Collective intelligence
    • Usage of pivot ontology based on Wikipedia (web-based encyclopedia that anyone can edit)
  • Windows Mobile
    isDevelopedBy
    Microsoft
    Windows Mobile
    isDevelopedBy
    Microsoft
    Omnia 2
    ISA
    smartphone
    Omnia 2
    ISA
    smartphone
    Flash memory
    ISA
    computer storage
    Flash memory
    ISA
    computer storage
    Omnia 2
    hasOS
    Windows Mobile
    Omnia 2
    hasOS
    Windows Mobile
    Omnia 2
    hasMemory
    Flash memory
    Omnia 2
    hasMemory
    Flash memory
    Introduction
    A working flow of Semantic Post-It
    Omnia 2 is a multimedia smartphone announced at Samsung. Omnia 2 runs Windows Mobile and comes with flash memory..
    Contents Space
    Flash memory is a non-volatile computer storage that
    can be electrically erased and reprogrammed.
    Windows Mobile is a compact mobile operating system developed by Microsoft
    Omnia 2 is a multimedia smartphone announced at Samsung. Omnia 2 runs Windows Mobile and comes with flash memory..
    Message Space
    Triple Message Space
    (Table information)
    hasMemory
    hasOS
    Linked Triple Message Space
  • Motivation
    Motivating Scenario
    Reading an article on “Omnia 2”
    Another similar Smartphone?
    More details on OS
    What is the recent trend of it?
    Company in competition
    What should we do?
  • Motivation
    Motivating Scenario
    CPU clock
    Reading an article on “Omnia 2”
    Another similar Smartphone?
    OS, platform
    More details on OS
    What is the recent trend of it?
    Manufacturer, design
    Company in competition
    Products of the company
    We have to think of what type of information are involved
  • Motivation
    Motivating Scenario
    nationality
    Reading an article on “Immanuel Kant”
    Where is he from?
    ?
    ?
    ?
    If new to philosophers,
    we are likely to have no idea about relevant information
  • Motivation
    What is the solution?
    • We need a system that retrieves relevant information
    • Data set that specifies attributes for each concepts is needed
    • Smartphone : manufacturer, OS, memory, …
    • Philosophers : nationality, follower, teacher, …
    • However, no one guy can describe every concepts
    • We can obtain the data set from collective intelligence
    author
    politician
    scientist
    engineer
    Philosophers
    Artist
  • Established
    February 16, 1971
    Type
    Government-run
    President
    Nam-Pyo Suh


    Motivation
    Wikipedia
    Wikipedia documents (2010/01/29)
    3,175,836 (ENG) - 11,527,437 users
    125,801 (KOR) - 100,498 users



    ① Inter-page link
    ② Inter-Language link
    ③ Category
    ④ Infobox: table information

  • Background Technologies
    New paradigm
    • A few years have passed since a new paradigm was introduced.
    • Semantic Web
    • A machine-readable web
    • Ontology
    • A formal specification of knowledge
  • Background Technologies
    Semantic Web
    • An evolving development of the World Wide Web
    • The meaning (semantics) of information and services on the web is defined
    • For the web to "understand" and satisfy the requests of people and machines to use the web content
    Our focus
    Adapted from Wikipedia
    (http://en.wikipedia.org/wiki/Semantic_Web)
  • Background Technologies
    RDF
    • Resource Description Framework
    A Wikipedia article about Tony Benn
    <http://en.wikipedia.org/wiki/Tony_Benn> <http://purl.org/dc/elements/1.1/title> "Tony Benn" .
    <http://en.wikipedia.org/wiki/Tony_Benn> <http://purl.org/dc/elements/1.1/publisher> "Wikipedia" .
    <rdf:RDF
    xmlns:rdf=http://www.w3.org/1999/02/22-rdf-syntax-ns#
    xmlns:dc=http://purl.org/dc/elements/1.1/>
    <rdf:Descriptionrdf:about=http://en.wikipedia.org/wiki/Tony_Benn>
    <dc:title>Tony Benn</dc:title>
    <dc:publisher>Wikipedia</dc:publisher>
    </rdf:Description>
    </rdf:RDF>
    An expression of “triple”
    Adapted from Wikipedia
    (http://en.wikipedia.org/wiki/Resource_Description_Framework)
  • Background Technologies
    Ontology
    A formal specification of knowledge to be interpreted by computers
    Company
    isManufacturedBy
    supportSoftware
    OS
    releaseDate
    rdfs:subPropertyOf
    runsOn
    cameraPixelOf
    Mobile Phone
    hasMemorySize
    supportOnlineSoftware
    hasWebsite
    Mobile PhoneSoftware
    rdfs:subClassOf
    rdfs:subClassOf
    rdfs:subClassOf
    PDA
    Cellular
    Phone
    SmartPhone
    Cellular
    Phone
    Smart Phone
    PDA
    Schema
    Instance
    releaseDate
    2008
    Samsung
    i900 Omnia
    cameraPixelOf
    5 megapixels
    supportOnlineSoftware
    hasWebsite
    Skype
    www.skype.com
    hasMemorySize
    128 MB
    runsOn
    Windows
    Mobile 6.1
    isManufacturedBy
    Samsung
  • Illustrative Example
    Content Space -> Message Space
    Semantic Post-It
    (Message List)
    Typical Web Browser
    External Contents
    KAIST is located in Daejeon, South Korea. KAIST was established by Korean government in 1971
    Scrap
    Related Problems : Mash-Up
    How to extract text from heterogeneous contents (in a context, not a scientific issue)
  • Illustrative Example
    Message Space -> Triple Message Space (1/2)
    Semantic Post-It
    (Message List)
    Semantic Post-It
    (Detail View)
    Semantic Post-It
    (Detail View)
    KAIST is located in Daejeon, South Korea. KAIST was established by Korean government in 1971
    KAIST is located in Daejeon, South Korea. KAIST was established by Korean government in 1971.… The current KAIST President Nam Pyo Suhtaught for…
    KAIST is located in Daejeon, South Korea. KAIST was established by Korean government in 1971.… The current KAIST President Nam Pyo Suhtaught for…
    Person
    Smatphone’s UI is limited. Information should be shown by one-click.
    Related Problems : ISA relation recognition
  • Estabilshed
    1971
    Province
    Daejeon
    Country
    South Korea


    Illustrative Example
    Message Space -> Triple Message Space(2/2)
    Semantic Post-It
    (Table View)
    Semantic Post-It
    (Message List)
    Semantic Post-It
    (Message View)
    KAIST is located in Daejeon, South Korea. KAIST was established by Korean government in 1971
    KAIST is located in Daejeon, South Korea. KAIST was established by Korean government in 1971.… The current KAIST President Nam Pyo Suhtaught for…
    KAIST
    Summarization
    Display size is too small to do full browsing.
    Related Problems : Triple extraction from text
  • Illustrative Example
    Triple Message Space ->Linked Triple Message Space
    Semantic Post-It
    (Graph View)
    Semantic Post-It
    (Message List)
    Semantic Post-It
    (Message View)
    Suh was born in Korea on April 22, 1936, and immigrated to the U.S. in 1954….
    KAIST is located in Daejeon, South Korea. KAIST was established by Korean government in 1971
    KAIST is located in Daejeon, South Korea. KAIST was established by Korean government in 1971.… The current KAIST President Nam Pyo Suhtaught for…
    president
    KAIST is located in Daedeok…
    province
    Daejeon is a center of transportation in South Korea, where two major,
    Relevant messages
    Display size is too small to show text
    Related Problems : Relevant keyword search by traversing Ontology
  • Illustrative Example
    Linked Triple Message Space
    Semantic Post-It
    (Using Ontology 1)
    Semantic Post-It
    (Using Ontology 2)
    Ontology 1
    Ontology 2
    Suh was born in Korea on April 22, 1936, and immigrated to the U.S. in 1954….
    Suh was born in Korea on April 22, 1936, and immigrated to the U.S. in 1954….
    University
    University
    president
    president
    president
    president
    KAIST is located in Daedeok…
    KAIST is located in Daedeok…
    Person
    Person
    province
    province
    province
    locatedAt
    Settlement
    Country
    Daejeon is a center of transportation in South Korea, where two major,
    South Korea is a presidential republic consisting of 16 administrative…
    Related Problems : Personal ontology editing, logical consistency checking
  • Illustrative Example
    Personal Ontology Editor
    • Rename the property name
    • If you wish to see another label in the link
    • Ex) isManufacturedBy -> manufacturer
    • Modify constraints
    • If you wish to see the country name rather than the city name
    • Ex)
    • Remove : University-province-Settlement
    • Add : University-locatedAt-Country
    • Use the modified ontology in your Semantic Post-It
    How to embed this complex UI into Smartphone?
    http://protege.stanford.edu/
  • System architecture (1/2)
    Message Interpretation Services
    HTTP request
    Semantic Post-IT Server
    (HTTP server)
    Semantic Post-IT client
    TABLEGEN
    CAT2ISA
    HTTP response
    Ontology Access
    DBpedia Access
    Personal Ontology
    Local Message DB
    External Message Service
    System Message DB
    Twitter, Blog, Email, Calendar, …
  • System architecture (2/2)
    Semantic Post-IT client
    • Local Message DB controller
    • Message input interface
    • Message list viewer
    • HTTP service controller
    • Semantic Post-IT server
    • External message service
    • Message relation graph viewer
    • Personal ontology editor
    Semantic Post-IT client
    Personal Ontology
    Local Message DB
  • Demo and Downloadable
    http://swrc.kaist.ac.kr/SemanticToolkits/
  • What is Semantic Post-It?
    Memo Admin Service
    Evernote, quickies, etc.
    Semantic Service Mash-Up
  • Semantic Service Mash-up
    Definition of 3 types of applications
    Type 1 Application: Information zooming on specific ‘word’ of a memo
    Type 2 Application: Memo Contents Analysis
    Type 3 Application: Information zooming on whole context of a memo
  • Type 1 Application: Example
    DEMO: Semantic Post-It
  • Type 2 Application: Demo
    DEMO: Semantic Post-It
  • Type 3 Application: Demo
    DEMO: Semantic Post-It
  • Structure of Semantic Post-It
    Post-It Server
    Post-It Client
    Service
    Repository
    Communication between
    Server and Client
    Provide application List
    Application Install
    Request for new application
    Execute
    application
    Request
    Ontology
    Ontology Request Module
    Enterprise Part:
    Add-on of
    Semantic Applications
    Shared Memo Request Module
    Return shared memos which the client have requested
    Can download shared memo to local database
    Request
    Shared Memo
    Add new memo
    Delete memo
    Find
    Related
    Memo
    Change memo
    Synchronization Module
    • Synchronization between Server & Client
    Tag memo
    Synchroni-zation
    Attach ontology to memo
    Shared
    Memo
    Ontology
    Repository
    Local File System
    Personal
    Memos
    Wikipedia Documents
    PURE PART
  • Support for Semantic Post-It:OntoCloud
    Ontology derived from Wikipedia infoboxes
    Official Website:http://swrc.kaist.ac.kr/ontocloud/
  • Support for Type 2 Application:Semantic Annotation
    One of possible type 2 application: Table-form summary generator
    Semantic Annotation: Mark on the documents – ‘which part’ could be transformed into table?
  • Semantic Annotation Toolkit: COAT
    DEMO: COAT
  • From annotated data to Application: Machine Learning Feature
    Support Vector Machine(SVM)
  • Ontology Feature
    Modern GSM-based BlackBerry handhelds incorporate an ARM 7 or 9 processor, while older BlackBerry 950 and 957 handheldsused Intel 80386 processors.
    IT Ontology Package
    Gathering semantic Info
    Using Ontology
    CPU
    Intel 80386
    Modern GSM-based BlackBerry handhelds incorporate an ARM 7 or 9 processor, while older BlackBerry 950 and 957 handheldsused Intel 80386 processors.
    useCPU
  • Data Authority Policy
    Annotators can check his/her documents ONLY!
    To prevent cheating
    Simple annotation data viewer is available
    For administrators
    DEMO: COAT Viewer
  • Support for Type 3 Application:300M Wikipedia articles into Database
    Provide baseline for shared memo
    For type 3 application
    Build shared memo database with 300M wikipedia articles as its part
  • Screenshots
    3) Table information extraction
    1) User inputs message
    2) Ontology recommendation
    4) Relevant message grouping
  • Enabling Technologies
    • CAT2ISA
    • Table Generator  
  • Technologies
    Ontology expression
    OWL (Web Ontology Language)
    <owl:Class rdf:ID=“Mobile Phone"/>
    <owl:Class rdf:ID=“PDA">
    <rdfs:subClassOf rdf:resource=“# Mobile Phone"/>
    </owl:Class>
    <owl:Class rdf:ID=“SmartPhone">
    <rdfs:subClassOf rdf:resource="# Mobile Phone"/>
    </owl:Class>
    <owl:Class rdf:ID=“Cellular Phone">
    <rdfs:subClassOf rdf:resource="# Mobile Phone"/>
    </owl:Class>
    <owl:Class rdf:ID=“Mobile Phone Software"/>
    <owl:ObjectProperty rdf:ID=“hasSoftware">
    <rdfs:domain rdf:resource="#Mobile Phone”/>
    <rdfs:range rdf:resource=“# Mobile Phone Software"/>
    </owl:ObjectProperty>
    <owl:ObjectProperty rdf:ID=“hasOnlineSoftware">
    <rdfs:subPropertyOf rdf:resource=“#hasSoftware"/>
    </owl:ObjectProperty>
  • Technologies
    Ontology inference
    Text
    Text
    Samsung releases Omnia
    Text
    Apple releases
    iPhone
    IPTV service is launched
    Environmental
    Technology
    Apple supports Green technologies
    support
    ISA
    beginService
    Service
    TV Service
    Company
    instanceOf
    instanceOf
    instanceOf
    manufacture
    use
    instanceOf
    Samsung
    Apple
    Product
    instanceOf
    beginService
    Green
    Technology
    IPTV
    support
    manufacture
    manufacture
    ISA
    ISA
    HDTV
    Device
    Omnia
    Software
    ISA
    instanceOf
    iPhone
    Smartphone
    instanceOf
  • Technologies
    Ontology construction from Wikipedia Infobox
    class
    instance
    properties
    university
  • Technologies
    Ontology construction from text
    2. Taxonomy Construction
    is-a
    3. Relation Addition
    not is-a
    1. Term extraction and conceptualization
    The other
    Final Ontology
    Existing Ontology
    Part-of
    equipment-of
    4. Integration
    5. Verification
    Part-of
    equipment-of
  • Technologies
    COAT (CoreOnto Annotation Toolkit)
    • Term and relation annotation
  • Technologies
    Ontology construction cost reduction
    Improve Ontology extension tech. and automation
    Web-scale annotation by ontology extension tech.
    2
    Ontology extension cost reduction by automation
    1
    Devise ontology extension tech.
    Cost reduction
    • Manual annotation cost reduction by using COAT
    • Further reduction could be possible if we can automate the process
    COAT
    Auto
    COAT
    Before COAT
  • CAT2ISA (cdh4696@world.kaist)
    • Technology for expanding semantic infrastructure
    • Extract semantic information from anonymous category system
  • CAT2ISA
    • Extract isa/instanceOf relation
    • A instanceOf B: A is a member of set B
    • A is called 'instance', B is called 'concept'
    • A and B must share 'essential properties': Properties that makes something as itself
    • Example: <Key-Sun Choi, instanceOf, Professor>: X<Key-Sun Choi, instanceOf, Human>: O
    • B isa C: B is a subset of C
     
    • isa/instanceOf relation: vital component in many semantic applications(e.g. semantic search, Q&A system, etc.)
     
     
  • Table Generator (cdh)
    • Summarize a text into table format based on its semantic tag
  • Table Generator
    • Information extraction using "Ontology"
    • Ontology: Formal representation of a set of concepts within a domain and the relationships between those concepts
    • Ontology-based information extraction:
  • Remaining for your home: references
    • History of Word Wide Web
    • Berners-Lee, Tim; Fischetti, Mark (1999). Weaving the Web. HarperSanFrancisco.
    • The Semantic Web
    • Berners-Lee, Tim; James Hendler and Ora Lassila (May 17, 2001). "The Semantic Web". Scientific American Magazine.
    • Grigoris Antoniou, Frank van Harmelen (March 31, 2008). A Semantic Web Primer, 2nd Edition
    • Ontology
    • Dean Allemang, James Hendler (May 9, 2008). Semantic Web for the Working Ontologist: Effective Modeling in RDFS and OWL. Morgan Kaufmann
  • Remaining for your home: Use experiences
    • [1] P. Mistry, P. Maes. Quickies: Intelligent Sticky Notes. In the Proceedings of 4th International Conference on Intelligent Environments (IE08). Seattle, USA. 2008
    • [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
    • [3] The Tabulator, http://www.w3.org/2005/ajar/tab
    • Read [1,2,3] and use the system [2,3]
    • Try also the following system
    • http://www.evernote.com/
    • Smartphone version is available
  • 2nd day
    • Deep story about semantic technology (20 min)
    • Wikipedia DbpediaOntocloud (kekeeo@world.kaist.ac.kr)
    • What are the upside?
    • Email 3.0
    • Information Zooming
    • Mobile hyperlink
    • Personal Preference Ontology and its use
    • Collective semantic intelligence of LOD + ontology cloud
    • Another demo (5 min)
    • What you can do immediately (review)
    • What you can contribute (review)
    • Big picture
    • Function, society
    • Technology to study
  • IT-Life Ontology
  • IT Campus Domain Ontology (Partial)
  • Wikipedia (http://en.wikipedia.org)
    • What is Wikipedia?
    • An online, collaboratively edited encyclopedia
    • Articles are available in over 250 languages
    • Freely available and freely distributable
    • Inter-language (interwiki) page links
  • DBpedia (http://dbpedia.org)
    • What is the DBpedia?
    • A community effort to extract structured information from Wikipedia
    • Available on the Web
    • Different types of structured information 
    • Infobox templates: summaries of the most relevant facts contained in an article
    • Categorization information
    • Images
    • Geo-coordinates
    • Links to external Web pages
  • OntoCloud
    • Our own constructed Ontology
    • Goals 
    • Making more intelligent IT systems focusing on devices and resources
    • Key classes
    • Device, Product, Resource, Technology, Person and Company
  • Structure of OntoCloud
    •  Template Ontology
    • Constructing the Pivot dataset
    • The infobox dataset from DBpedia3.4 (semi-automated)
    • IT CUO (IT Core Upper Ontology)
    • A middle level ontology for integration
    • Ontologies under IT domains
    • IT Service Ontology
    • IT Device Ontology
    • IT Core Ontology 
  • Mobile 3.0 and its Requirements (full picture: jha)
    • Email 3.0
    • Information Zooming
    • Mobile hyperlink
    • Personal Preference Ontology and its use
    • Collective semantic intelligence of LOD + ontology cloud
  • E-mail 3.0(email categorization)
    Automatically map into a class in ontology
    Related Problems
    • Topic detection
    Current Status
    • Categorization of long and well-formed text (e.g. Wikipedia documents)
    Challenges
    • Short message interpretation
    • Personal writing styles
  • E-mail 3.0(Recipient recommendation)
    Automatically recommend person to whom the message should be sent
    sender@abc.com
    Challenges
    • Task Ontology modeling
  • E-mail 3.0(Relevant information attachment)
    Automatically attach pictures
    sender@abc.com
    Automatically attach files in local disk
    Challenges
    • Semantic tags on multimedia data
    • Local file indexing
    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,
  • Topic
    Information retrieval
    Deadline
    Mar 30, 2010
    Organizer
    Benno Stein
    E-mail 3.0(Mash-up Services)
    A message in inbox
    Automatically create to-do list
    The following list organizes classic and ongoing topics from the field
    of text-based IR for which contributions are welcome:
    - Theory. Retrieval models, language models, similarity measures,
    formal analysis
    - Mining and Classification. Category formation, clustering, entity
    resolution, document classification
    ---------------------------------------------------------------------------
    Important Dates:
    ---------------------------------------------------------------------------
    Mar 30, 2010 Deadline for paper submission
    Apr 20, 2010 Notification to authors
    May 17, 2010 Camera-ready copy due
    Aug 30, 2010 Workshop opens
    ---------------------------------------------------------------------------
    Workshop Organization:
    ---------------------------------------------------------------------------
    Benno Stein, Bauhaus University Weimar
    Michael Granitzer, Know-Center Graz & Graz University of Technology
    Contact: tir@webis.de
    Information about the workshop can be found at http://www.tir.webis.de
    Related Problems:
    • Table information extraction
    • Mash-up
    Current Status
    • Table information generation from text
    Challenges
    • Table information generation from semi-structured text
  • Information Zooming
    •  What is information zooming?
    • Show small amount of information first
    • When user requires more information about one part, shows more detailed information about that part.
    • Why is it necessary?
    • Mobile environment: small display
    • We cannot show all the necessary information at once! (Lack of space)
  • Information Zooming in Semantic Post-It
    •  Information zooming for one word
     
     
     
     
     
     
    • Information zooming for whole memo
  • Mobile hyperlink
    •  What is mobile hyperlink?
    • Represent URL as barcode
    • Take a picture of the barcode using camera in cellphone and you move to that URL!
    •  Why is it necessary?
    • Mobile environment: small interface
    • Hard to type all the URL
    • Example of mobile hyperlink
     
     
    • QR code:
                               
  • Personal Preference Ontology and its use
    • Task of packaging from a potentially large ontology, one or several significant sub-parts
    • Knowledge sharing and re-use crucial research issues
     
    • On-demand Extraction Service
    • Takes a concept and extract the relations
    •  
    • Interactive Service
    • The user have to select class and relations to consider
  • Collective semantic intelligence of LOD + ontology cloud
  • The Linked Open Data Cloud
  • What you can do immediately
    review
    discussion
  • What you can contribute
    • Data Synchronization for Mobile applications
    • Synchronization is a data transfer between computer and mobile device that aims to keep both of components in a coherent state
    • Knowledge-driven Security Handling for Mobile Applications
    • Several mobile applications attacks have beenrecently reported
    • Device  and environment
    • Ontology Packaging for Mobile field
    • Bacause of its physical aspect, a mobile device has a limited processing and computing capabilities
  • Big picture
    • Function, society and Technology to study
  • Windows Mobile
    isDevelopedBy
    Microsoft
    Windows Mobile
    isDevelopedBy
    Microsoft
    Omnia 2
    ISA
    smartphone
    Omnia 2
    ISA
    smartphone
    Flash memory
    ISA
    computer storage
    Flash memory
    ISA
    computer storage
    Omnia 2
    hasOS
    Windows Mobile
    Omnia 2
    hasOS
    Windows Mobile
    Omnia 2
    hasMemory
    Flash memory
    Omnia 2
    hasMemory
    Flash memory
    Big Picture
    A working flow of Semantic Post-It
    Contents Space
    Omnia 2 is a multimedia smartphone announced at Samsung. Omnia 2 runs Windows Mobile and comes with flash memory..
    Flash memory is a non-volatile computer storage that
    can be electrically erased and reprogrammed.
    Windows Mobile is a compact mobile operating system developed by Microsoft
    Message Space
    Omnia 2 is a multimedia smartphone announced at Samsung. Omnia 2 runs Windows Mobile and comes with flash memory..
    Triple Message Space
    (Table information)
    Linked Triple Message Space
    hasMemory
    hasOS
    What is the next step?
  • Windows Mobile
    isDevelopedBy
    Microsoft
    Omnia 2
    ISA
    smartphone
    Flash memory
    ISA
    computer storage
    Omnia 2
    hasOS
    Windows Mobile
    Omnia 2
    hasMemory
    Flash memory
    Big Picture
    Message generation
    Linked Triple Message Space
    hasMemory
    hasOS
    Personalized Message Space
    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.
    How to do so?
    Do you have an idea how to utilize personalized ontology to generate sentences?
    Personalized ontology
  • Established
    1971
    Province
    Daejeon
    Country
    South Korea


    Big Picture
    Functions (1/2)
    • From text to presentation file
    • Challenges
    • Semantic Tagging to Image
    • Refer to http://www.image-net.org/
    KAIST is located in Daejeon, South Korea. KAIST was established by Korean government in 1971
    KAIST
    established
    1971
    province
    Daejeon
    Country
    South Korea
    Table information
    Table information + images
  • Big Picture
    Functions (2/2)
    • From table to text
    • Generate NL text by traversing table
    • KAIST-Province-Daejeon
    • Daejeon-Districts-fifth
    •  KAIST is located in Daejeon. Daejeon is the fifth largest city in the country.
    • Challenges
    • Transform a predicate into verb phrases
    • Ex) Province -> is located in
  • Big Picture
    Society
    Message Interpretation Services
    HTTP request
    Semantic Post-IT Server
    (HTTP server)
    Semantic Post-IT client
    TABLEGEN
    CAT2ISA
    HTTP response
    Personal Ontology
    Ontology Access
    DBpedia Access
    Local Message DB
    OpenAPI generator
    Make your own message interpretation modules and upload it.
    OpenAPI generator will make it available as an OpenAPI service.
  • Big Picture
    Technologies to study(interdisciplinary)
    Architecture
    /Urban design
    Software Engineering
    Graphics
    IR, AI, MachineLearning
    HCI
    CognitiveScience
    Internet of Things
    Cloud Computing
    Design
    DB &
    Data Mining
    ConvergenceNetworks
    Sociology
    Middleware
    Handle huge amount of messages
    Ex) manipulating Wikipedia documents
    Find person of my interests
    Ex) References in papers
    Which layout is suitable for the display?
    Ex) Table-memo for a tiny display
    Plug-in architecture
    Ex) Collect personal documents by using Google Desktop APIs
    How to extract table data from memo?
    Ex) information extraction from document
    Which type of memo? Writing style anaylsys
    Ex) To-do list, contact, documents
    Write message anywhere and anytime
    Ex) RFID-equipped notes
  • Deep story about semantic technology
    discussion!
  • Credits
    Dong-Hyun Choi, cdh4696@world.kaist.ac.kr
    Eun-Kyung Kim, kekeeo@world.kaist.ac.kr
    JinhyunAhn, jhahn@world.kaist.ac.kr
    Key-Sun Choi, kschoi@kaist.edu
    http://swrc.kaist.ac.kr/ontocloud
    http://swrc.kaist.ac.kr/SemanticToolkits/