CT-DA: A Knowledge Extraction Method for Cultural Industry Big Data

Abstract

Knowledge extraction is the core work of constructing a knowledge graph, but most knowledge extraction methods assume perfect data support. Therefore, this paper analyzes the characteristics of big data in the cultural industry. In addition to the consensus characteristics of big data, these data also highlight the features of sectors such as low resources and intense data boundary fuzziness. Therefore, this paper proposes a knowledge extraction method for cultural industry data (CT-DA). Firstly, design a labeling strategy for big data in the cultural industry. Secondly, according to the low resource characteristics of data, create the counter transfer learning layer to realize resource transfer. Considering the intense fuzziness of data, design the dynamic attention mechanism layer for learning the critical attention of entities in the cultural field. Finally, build an experimental platform. The experiments show that this method has performance advantages in accuracy, recall, and F1.