Online Indices for Predictive Top-k Entity and Aggregate Queries on Knowledge Graphs

Abstract

Knowledge graphs have seen increasingly broad applications. However, they are known to be incomplete. We define the notion of a virtual knowledge graph which extends a knowledge graph with predicted edges and their probabilities. We focus on two important types of queries: top-k entity queries and aggregate queries. To improve query processing efficiency, we propose an incremental index on top of low dimensional entity vectors transformed from network embedding vectors. We also devise query processing algorithms with the index. Moreover, we provide theoretical guarantees of accuracy, and conduct a systematic experimental evaluation. The experiments show that our approach is very efficient and effective. In particular, with the same or better accuracy guarantees, it is one to two orders of magnitude faster in query processing than the closest previous work which can only handle one relationship type.