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Interactive visual analytic systems enable users to discover insights fromcomplex data. Users can express and test hypotheses via user interaction,leveraging their domain expertise and prior knowledge to guide and steer theanalytic models in the system. For example, semantic interaction techniquesenable systems to learn from the user's interactions and steer the underlyinganalytic models based on the user's analytical reasoning. However, an openchallenge is how to not only steer models based on the dimensions or featuresof the data, but how to add dimensions or attributes to the data based on thedomain expertise of the user. In this paper, we present a technique forinferring and appending dimensions onto the dataset based on the priorexpertise of the user expressed via user interactions. Our technique enablesusers to directly manipulate a spatial organization of data, from which boththe dimensions of the data are weighted, and also dimensions created torepresent the prior knowledge the user brings to the system. We describe thistechnique and demonstrate its utility via a use case.

Adding Semantic Information into Data Models by Learning Domain Expertise from User Interaction