Reachability Queries with Label and Substructure Constraints on Knowledge Graphs
Xiaolong Wan, Hongzhi Wang · IEEE Transactions on Knowledge and Data Engineering · 2022
Since knowledge graphs (KGs) describe and model the relationships between entities and concepts in the real world, reasoning on KGs often correspond to the reachability queries with label and substructure constraints (LSCR queries). Specifically, for a search path p, LSCR queries not only require that the labels of the edges passed by p are in a certain label set, but also claim that a vertex in p could satisfy a certain substructure constraint. They are much more complex than existing label-constraint reachability (LCR) queries. LSCR queries on KGs can be addressed by two natural ways (EA-1) an online search algorithm and (EA-2) a combined search strategy, to the best of our knowledge. This paper presents two optimized algorithms for EA-1 and EA-2, but the optimized algorithms are still inefficient, since their efficiencies are highly dominated by their search directions as analyzed in this paper. Motivated by that, this paper presents an efficient informed search strategy on KGs, named INSK, with a lightweight index, named local index. An extensive experimental evaluation, on both synthetic and real KGs, illustrates that our INSK can efficiently process LSCR queries on KGs.