Knowledge Fusion Analysis and Research Based on Knowledge Graph

Abstract

With the rapid popularization of Internet technology, it is a new challenge to mine the hidden knowledge behind the data in an organized and directed way. Knowledge graph not only describes the content of semantic associations and entities existing in the objective world, but also uses graph structure to visually present the structured knowledge system for system users. Therefore, in the development of modern technology, knowledge graph has been widely concerned by industry and academia. In the era of big data, in order to build high-quality knowledge graph, knowledge fusion is very critical, among which entity alignment and entity link are important parts of knowledge fusion task. Starting from the field of film and television, this paper uses the entity alignment technology of NovEA model and the candidate entity ranking structure based on CNN-DSSM to deeply discuss the key technologies of knowledge fusion during the construction of multi-level film and television knowledge graph. The final experimental results prove that, compared with other models of the same task, the NovEA model studied in this paper has higher alignment accuracy, and the candidate entity ranking structure based on CNN-DSSM is better designed.