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Research enabling other research  Infinite graph (UTD) is the prerequisite for EL++ (RPI, HP), Expanded Visualization (UCSB) and Topic Modeling (UC Irvine,

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Presentation on theme: "Research enabling other research  Infinite graph (UTD) is the prerequisite for EL++ (RPI, HP), Expanded Visualization (UCSB) and Topic Modeling (UC Irvine,"— Presentation transcript:

1 Research enabling other research  Infinite graph (UTD) is the prerequisite for EL++ (RPI, HP), Expanded Visualization (UCSB) and Topic Modeling (UC Irvine, RPI)‏  Workflow (Occulus, Cal)‏, Ontology homogenization Infinite Graph Visualization EL++ Topic Modeling Workflow

2 Entity Resolution  Entity Resolution (UMass) is the prerequisite for all research and large flows Ontology John Updated Ontology Rocker Entity 1 Entity 2 Is A Machine Learning

3 Entity Resolution  Unique Approach – chaotic at all points. Variable vocabulary and ontology.  Change in input ontology changes result.  Machine learning.  Production quality only needing packaging.

4 Entity Resolution  Unique Approach – chaotic at all points. Variable vocabulary and ontology.  Change in input ontology changes result.  Machine learning.  Production quality only needing packaging.

5 Infinite Graph  Unique Approach – works at the model level. Appears as if it is working in the jvm.  Leverages work from previous generations of technology.  Learned from others failures.  Complete and in repository.  Allows addressing of other problems.

6 Visualization  No limits on nodes  Natively visualizes RDF  Scales in a browser  No plugins  Unmatched aestetics  Specification compliant leveraging google paradigm

7 Ontology Homogenization  World of many stores ontologies and choices  Distributed document retrieval  Machine learning automation  Partner with Raytheon  Solution next year (design solved)‏

8 Algorithms  Design consistent  Model to Model  Variable vocabularies  Self-addressed stamped envelope  Complete  Adding to code base without source code (JEE deployer role)‏

9 Thinking Outside the Box  Entity Resolution (Ontology as Input, Ontology updated through machine learning)‏  Infinite Graph (Graph Oriented Virtualization Algorithms)‏  EL++ (Polynomial Based Reasoning)‏  Expanded Visualization (2D rendering, virtualized memory)‏  Topic Modeling (Chaotic Use of Vocabularies)‏

10 Recognition  Entity Resolution (McCallum)‏  Infinite Graph (Khadilkar)‏  EL++ (Seaborne, Zaki)‏  Expanded Visualization (Hollerrer)‏  Algorithms (Zaki, Smyth)‏  Workflow (Day,Zysman)‏  Ontology Homogenization (Partyka)‏


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