Dynamic Relation Repairing for Knowledge Enhancement

Abstract

Dynamic relation repair aims to efficiently validate and repair the instances for knowledge graph enhancement (KGE), where KGE captures missing relations from unstructured data and leads to noisy facts to the knowledge graph. With the prosperity of unstructured data, an online approach is asked to clean the new RDF tuples before adding them to the knowledge base. To clean the noisy RDF tuples, graph constraint processing is a common but intractable approach. Plus, when adding new tuples to the knowledge graph, new graph patterns would be created, whereas the explicit discovery of graph constraints is also intractable. Therefore, although the dynamic relation repair has an unfortunate hardness, it is a necessary approach for enhancing knowledge graphs effectively under the fast-growing unstructured data. Motivated by this, we establish a dynamic repairing and enhancing structure to analyze its hardness on basic operations. To ensure dynamic repair and validation, we introduce implicit graph constraints, approximate graph matching, and linkage prediction based on localized graph patterns. To validate and repair the RDF tuples efficiently, we further study the cold start problems for graph constraint processing. Experimental results on real datasets demonstrate that our proposed approach can capture and repair instances with wrong relation labels dynamically and effectively.