PaTeCon: A Pattern-Based Temporal Constraint Mining Method for Conflict Detection on Knowledge Graphs
This addresses the challenge of maintaining temporal consistency in knowledge graphs for researchers and practitioners, offering an automated solution to reduce labor-intensive manual work.
The paper tackles the problem of detecting temporal conflicts in knowledge graphs by proposing PaTeCon, a pattern-based method that automatically mines temporal constraints from data, eliminating the need for manual enumeration. Experimental results on Wikidata and Freebase datasets demonstrate its effectiveness in generating valuable constraints.
Temporal facts, the facts for characterizing events that hold in specific time periods, are attracting rising attention in the knowledge graph (KG) research communities. In terms of quality management, the introduction of time restrictions brings new challenges to maintaining the temporal consistency of KGs and detecting potential temporal conflicts. Previous studies rely on manually enumerated temporal constraints to detect conflicts, which are labor-intensive and may have granularity issues. We start from the common pattern of temporal facts and constraints and propose a pattern-based temporal constraint mining method, PaTeCon. PaTeCon uses automatically determined graph patterns and their relevant statistical information over the given KG instead of human experts to generate time constraints. Specifically, PaTeCon dynamically attaches class restriction to candidate constraints according to their measuring scores.We evaluate PaTeCon on two large-scale datasets based on Wikidata and Freebase respectively. The experimental results show that pattern-based automatic constraint mining is powerful in generating valuable temporal constraints.