Construction of event knowledge graph based on semantic analysis

Abstract

At present, the research and application of enterprise credit event information mainly takes the data of enterprise credit events as a dimension of enterprise credit evaluation, and lacks in-depth analysis and mining of the content of special events. On the basis of sorting out the connotation of enterprise credit events, the article firstly proposes a model with evolutionary features, network structured features and unstructured features of text data for the knowledge graph of enterprise credit events; then, the events in enterprise credit events are extracted in the form of case study, the named entities and dependency relationships in the text statements are analyzed, and the events with subject-predicate object relationship as the main form are extracted; secondly, the statements are analyzed. Finally, the extracted events and relationships are matched to form a knowledge graph of corporate credit events. The study applies the mapping research method to the field of corporate credit event research, and realizes the process analysis of the evolution of corporate credit events using knowledge graph.