Making Sense of Scales Using a Database of Measurements

Datalab Faculty

Jessica Hullman

Project Description

Measurements are ubiquitous yet can be challenging to understand when the unit (e.g., decaliters, tons) or magnitude (e.g., 320m, $5 mil) are unfamiliar to us. Strategies like re-unitization, in which an unfamiliar measurement is re-expressed using a new unit (e.g., 10kg is equal to the weight of 2 printers), can aid understanding but often require a skilled designer to realize. These re-expressions can also be personalized given some information about the user, such as their location (e.g., 11 miles is twice the distance from your house to the Space Needle). This project develops databases of familiar objects and landmarks and their measurements, drawing on web crawling techniques,  semantic databases like WordNet and ImageNet, object databases like Amazon and Wikipedia, and crowdsourcing. We design automated algorithms for strategies like re-unitization and proportional analogy that rank re-expressions based on a number of dimensions. We apply these automated strategies in web applications that allow a user to get on-demand re-expressions of complex measurements.