Journal of King Saud University: Computer and Information Sciences (Dec 2023)
SMAAMA: A named entity alignment method based on Siamese network character feature and multi-attribute importance feature for Chinese civil aviation
Abstract
With the growth of demand for air-space integrated transportation industry, the civil aviation transportation industry plays an increasingly important role in the national economy and transportation. The diversity and diversified development of civil aviation emergencies have greatly affected the decision-making efficiency for emergencies in civil aviation emergency management systems. Constructing a knowledge graph of civil aviation and improving the reliability and richness of the knowledge graph has become an urgent problem to be solved. There are some problems during the construction of a civil aviation domain knowledge graph such as long entity length, existing hybrid and composite entities, the similarity of the font features of domain entity names, a difference of information between entities, separated coding between entities, and error-prone in the transmission process of coding. So we research the construction method of knowledge graph for civil aviation emergencies to resolve these problems. (1) We propose a font pre-training model and incorporate a character feature layer into the embedding layer. (2) We propose a multi-attribute attention alignment method based on the Siamese network; entities with similar structures in the civil aviation knowledge base are input into the font layer for pre-training; the complete semantic information of entities and the importance of different attributes to entities are learned through two sub-networks respectively to improve the integrity of civil aviation knowledge graph. Experiments were carried out on the self-built aviation emergency and public data sets, respectively. The experimental results show that compared with other models, the F1 value of the proposed model can reach 96.97%, which verifies the feasibility of the model.
