KartGPS: Knowledge Base Update with Temporal Graph Pattern-based Semantic Rules

Hao Xin, Lei Chen

Abstract: The rapidly changing nature of information world-wide often leads to incomplete and obsolete knowledge facts stored in knowledge bases (KBs). Therefore, reasoning over the dynamic KB sequences, which targets at knowledge inference from evolving facts, is of great importance to maintain KB completeness as well as freshness. Existing approaches for KB updating mainly either focus on knowledge representation learning methods, which suffer from lack of interpretability, or attempt to mine path-based logical rules, which are limited in capturing structural semantics of KB. In this work, we present KartGPS, a system for KB updating taking advantage of temporal graph pattern-based semantic (tGPS) rules. Specifically, the tGPS rules are learned from KB sequences and thus are capable of capturing both temporal and topological regularities of KBs along the evolving of time. Due to the huge amount and imperfect quality of tGPS rules, directly generating and applying all generated rules in a brute-force manner for knowledge updating over large-scale KB sequences would be highly time-consuming and error-prone. Therefore, we investigate the problem of Knowledge Update Rule Discovery (KURD), which aims at deriving an optimal subset of tGPS rules for performing knowledge updating, considering the rule quality and coverage. We show that the KURD problem is NP-hard and design two effective approximation algorithms with greedy and pruning strategies. We demonstrate the effectiveness and efficiency of proposed approaches by extensive experiments on real-world KB datasets.