Optimizing Hyper-Phrase Queries

Abstract

A hyper-phrase query (HPQ) consists of a sequence of phrase sets. Such queries naturally arise when attempting to spot knowledge graph (KG) facts or sets of KG facts in large document collections to establish their provenance. Our approach addresses this challenge by proposing query operators to detect text regions in documents that correspond to the HPQ as combinations of n-grams and skip-grams. The optimization lies in identifying the most cost-efficient order of query operators that can be executed to identify the text regions containing the HPQ. We show the efficiency of our optimizations on spotting facts from Wikidata in document collections amounting to more than thirty million documents.