Generating hierarchical ontologies

Abstract

Described herein are embodiments for machine-generating and naming ontologies for for-sale items. A neural network may be used to train information describing for-sale items into feature vectors that describe the for-sale items. These feature vectors may be sorted into clusters based on their relative proximity using clustering algorithms. The resulting clusters may be sub-divided into smaller clusters depending on the precision used in the clustering algorithm. The set of clusters may form a hierarchical data structure where each level has clusters determined at a certain precision and each lower level sub-divides those clusters. The clusters may be named based on the most salient facets that describe the for-sale items in the clusters.