Abstract:This work proposes and evaluates a novel approach to determine interesting categorical attributes for lists of entities. Once identified, such categories are of immense value to allow constraining (filtering) a current view of a user to subsets of entities. We show how a classifier is trained that is able to tell whether or not a categorical attribute can act as a constraint, in the sense of human-perceived interestingness. The training data is harnessed from Web tables, treating the presence or absence of a t… Show more
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