Chinese satellite frequency and orbit entity relation extraction method based on dynamic integrated learning

Abstract

Given the scarcity of Satellite Frequency and Orbit (SFO) resources, it holds paramount importance to establish a comprehensive knowledge graph of SFO field (SFO-KG) and employ knowledge reasoning technology to automatically mine available SFO resources. An essential aspect of constructing SFO-KG is the extraction of Chinese entity relations. Unfortunately, there is currently no publicly available Chinese SFO entity Relation Extraction (RE) dataset. Moreover, publicly available SFO text data contain numerous NA (representing for “No Answer”) relation category sentences that resemble other relation sentences and pose challenges in accurate classification, resulting in low recall and precision for the NA relation category in entity RE. Consequently, this issue adversely affects both the accuracy of constructing the knowledge graph and the efficiency of RE processes. To address these challenges, this paper proposes a method for extracting Chinese SFO text entity relations based on dynamic integrated learning. This method includes the construction of a manually annotated Chinese SFO entity RE dataset and a classifier combining features of SFO resource data. The proposed approach combines integrated learning and pre-training models, specifically utilizing Bidirectional Encoder Representation from Transformers (BERT). In addition, it incorporates one-class classification, attention mechanisms, and dynamic feedback mechanisms to improve the performance of the RE model. Experimental results show that the proposed method outperforms the traditional methods in terms of F1 value when extracting entity relations from both balanced and long-tailed datasets.