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semantic search engine in a multimedia knowledge base with natural language input

semantic search engine in a multimedia knowledge base with natural language input

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    E-Librarian Service E-Librarian Service Presentation Transcript

    • Natural language search in multimedia knowledge bases
      Serge Linckels & Christoph Meinel
      Hasso Plattner Institute
      at Potsdam University
    • Hasso Plattner Institute
      2
      Founded in 1998
      10 professors
      100 lectures, co-workers…
      IT-Systems Engineering (Bachelor, Master, PhD)
    • 3
    • Classical approach
      4
      strong AI 
      • Logical reasoning
      • Knowledge representation
      • Natural Language Processing
      • Machine Learning
      weak AI 
      artifical intelligence (AI)
      art or porn?
    • Web 2.0 approach
      5
      voting
      tagging
      art or porn?
      photography
      woman
      black & white
      18-200mm VR
      best picture 2010
      Statistical solution
      Requires critical mass of "good" users
    • Semantic Web approach
      6
      art or porn?
      artistic black and
      white picture
      of a nakedwoman
      in a narrowstreet
    • Keyword matching
      7
      user query
      black
      and
      white
      of
      a
      woman
      in
      picture
      naked
      narrow
      street
      photo
      nude
      outdoors daylight
      search engine
      data
      metadata
      +
      artistic black and
      white picture
      of a nakedwoman
      in a narrowstreet
    • Keyword search
      8
      too much information
      false interpretation
      false positive
    • Keyword matching problems
      9
      black and white photo of a nude woman outdoors in daylight
      of black woman in a white daylight photo and nude outdoors
      does order matters?
      black and white photo of a nude woman outdoors indaylight
      does size matters?
    • Syntax tree
      10
      Informal language
      a date
      Brown tag set
      noun phrase (NP), prepositional phrase (PP), adjective phrase (ADJP), verb phrase (VP)
      adjective (JJ), conjuncation (CC), preposition (IN), determiner (DT), noun (NN), verb (VBZ)
      21
      March
      2011
      a date
      presentation
      CRP-HT
    • Natural language processing
      11
      semantic interpretation
      S  Photo hasColor.BW
      photoOf.(Woman isNude)
       isOutdoors.Daylight
      description logics
      remove “stop words”
      verbs, adjectives, adverbs  roles
      nouns  concepts
    • Martching of concept descriptions
      12
      artistic black and
      white picture
      of a naked woman
      in a narrow street
      P  Picture  hasColor.BW  isArtistic  pictureOf.(Woman  isNaked)  isLocated.(Street  isNarrow)
      ?
      similarity
      Q  Photo  hasColor.BW  photoOf.(Woman  isNude)
       isOutdoors.Daylight
    • Ontological approach
      13
      picture
      image
      photo
      movie
      semantic resources
      e.g., WordNet
      pictureOf  photoOf
      isNude  isNaked
      equivalences
      Photo  Picture
      Photo is subsumed by Picture
      P  Picture  hasColor.BW  isArtistic  pictureOf.(Woman  isNaked)  isLocated.(Street  isNarrow)
      ?
      similarity
      Q  Photo  Picture  hasColor.BW  pictureOf.(Woman
       isNaked)  isOutdoors.Daylight
      Q  Photo  hasColor.BW  photoOf.(Woman  isNude)
       isOutdoors.Daylight
    • Semantic distance
      14
      Query
      Object 1
      1
      2
      Object 2
      no cover
      3
      Object 3
      Object 4
      Object 5
      cover
      rest
      miss
      Best cover = object with smallest rest and miss
      preference is given to smallest miss
      miss
    • Annotating motion picture
      15
      Lecture "WWW Grundlagen" by Prof. Meinel
      #1
      Intro
      #n
      How TCP/IP works
      #2
      Protocols in general
      #3
      Error-handling as task of a protocol
      #4
      Error-handling
      LO3 Protocol hasTask.ErrorHandling
      This clip is about
      error-handling as
      a task of a protocol
      <owl:Class rdf:about="#LO3">
      <owl:intersectionOf rdf:parseType="Collection">
      <owl:Class rdf:about="#Protocol" />
      <owl:restriction>
      <owl:onProperty rdf:resource="#hasTask" />
      <owl:someValuesFrom rdf:resource="#ErrorHandling" />
      </owl:restriction>
      </owl:intersectionOf>
      </owl:Class>
    • Illustration
      16