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TANGO Table ANalysis for Generating Ontologies

TANGO Table ANalysis for Generating Ontologies. repeat: understand table generate mini-ontology match with growing ontology Adjust & merge until ontology developed. Growing Ontology.

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TANGO Table ANalysis for Generating Ontologies

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  1. TANGOTable ANalysis for Generating Ontologies • repeat: • understand table • generate mini-ontology • match with growing ontology • Adjust & merge • until ontology developed Growing Ontology TANGO in a nutshell: TANGO repeatedly turns raw tables into conceptual mini-ontologies and integrates them into a growing ontology.

  2. Integrating and Storing Uncertain Data

  3. Teach a robot new tricks Avoid Obstacle Pick up Plan path Navigate Point at Generate distribution Look happy Imitate Identify Object Wave Follow Look at Nod Hear Speak Localize Look at Find face Wave Nod Speak Find face Identify Apple Look at Point at Speak Reward Greet Maintain Distance Look happy Maintain Distance Greet Speak Imitate Reward • Basic Skills: • movement • capability • logical • behavioral … … … … Robot: Azimo Safety Layer: Default Layer: Task: Play Imitation Game Assemble Want to play a game! Let’s imitate her! Assemble Clinician: Lee

  4. Documents Patterns Results Sorted A B C Aaron David … Aarons George S … Abbott Charles H … … Layout W. S. NEWBURY W. H. ADAMS JOSEPH BACHMAN … • Logical • Name • Title • City T. M. Gatch E. H. Stolte W. S. Newbury …

  5. Human Behavior • Fire Escape Placement • Fugitive Chase Simulation • Adaptive Fire-fighting Government Professionals • Truly Dynamic Behavior • Graphics • Machine Learning • Simulation • Animal Learning • Parent-Child Learning Transfer • Predator Introduction • Mutual Genetic Adaptation Biologists • Movies and Games • 1,000+ Asymmetric Actors • Human-like AI Adaptation • Screen-based Intelligence Entertainment Industry Brian Ricks - BYU CS - October, 2009

  6. Incremental Multi-label Classification with Unknown Labels Happy Bright Peaceful Dark Gloomy We don't know which labels we might encounter nor how many labels there will be during training. We need to be able to dynamically add new labels into our learning model. ? ? ? . . . Feature Extraction Happy, Bright, Energetic,?,?,?,?, ... Wet,Happy,?,?,?,... We cannot assume implicit negativity for images with missing labels. Dark, Gloomy,?,?,?,...

  7. 0/9 algorithms • Heuristics • DN=1 • CL=0.02 • DS=0.01 • MV=0 • Type: Outlier

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