T2KG: An End-to-End System for Creating Knowledge Graph from Unstructured Text

Knowledge Graph (KG) plays a crucial role in many modern applications. Nevertheless, constructing KG from unstructured text is a challenging problem due to its nature. Consequently, many approaches propose to transform unstructured text to structured text in order to create a KG. Such approaches cannot yet provide reasonable results for mapping an extracted predicate to its identical predicate in another KG. Predicate mapping is an essential procedure because it can reduce the heterogeneity problem and increase searchability over a KG. In this paper, we propose T2KG system, an end-to-end system with keeping such problem into consideration. In the system, a hybrid combination of a rule-based approach and a similarity-based approach is presented for mapping a predicate to its identical predicate in a KG. Based on preliminary experimental results, the hybrid approach improves the recall by 10.02% and the F-measure by 6.56% without reducing the precision in the predicate mapping task. Furthermore, although the KG creation is conducted in open domains, the system still achieves approximately 50% of F-measure for generating triples in the KG creation task.