SortingHat: A framework for deep matching between classes of entities

This paper addresses the problem of “deep matching” - or matching different classes of entities based on latent underlying semantics, rather than just their visible attributes. An example of this is the “automatic task assignment” problem where several tasks have to be assigned to people with varied skill-sets and experiences. Datasets showing types of entities (tasks and people) along with their involvement of other concepts, are used as the basis for deep matching. This paper describes a work in progress, of a deep matching application called SortingHat. We analyze issue tracking data of a large corporation containing task descriptions and assignments to people that were computed manually. We identify several entities and concepts from the dataset and build a co-occurrence graph as the basic data structure for computing deep matches. We then propose a set of query primitives that can establish several forms of semantic matching across different classes of entities.