Dynamic Relation Repairing for Knowledge Enhancement
Rui Kang, Hongzhi Wang · IEEE Transactions on Knowledge and Data Engineering · 2021
As the prosperity of unstructured data in networks, knowledge extraction tools have been designed for new knowledges from unstructured data streams. The generated RDF streams by knowledge extraction are always containing much errorous tuples causing inconsistency to knowledge graph engine.To enable the completeness of information from unstructured streams, dynamically repairing the violated RDF tuples is the best way to process. Observed this, we propose dynamic relation repair process to find and eliminate violations in errorous RDF stream. RDF data, arranged as graphs, leads to computation hardness when trying to find constraints and repairing metrics. In this paper, we consider graph repairing process with implicit graph constraints enabling RDF candidates validation and repairing through subgraph matching with the sample of localized subgraphs from graph engine with the same relation labels. We also propose approximated graph matching process through dynamic graph embedding for time efficiency. Cold start problem is also well analyzed to avoid inefficient repairing. Experimental results on real datasets demonstrate that our work can capture and repair violation in RDF streams dynamically and effectively.