Yi Fan

Airworthiness directives contain rich, standardized information critical for diagnosing aircraft faults. However, the complexity, domain specificity and heterogeneous data characteristics of these texts make it difficult to extract and structure this knowledge into an organized fault knowledge graph (KG), thereby limiting progress toward intelligent civil aviation maintenance and management. To address this challenge, we propose a large language model (LLM) fine-tuning approach that integrates domain knowledge to mine fault knowledge from Chinese Airworthiness Directive (CAD) texts. After comprehensive text preprocessing and expert-guided manual annotation, we constructed a specialized dataset for aircraft fault knowledge discovery, encompassing named entity recognition (NER) and relation extraction (RE) tasks. The LLM was fine-tuned through parameter-efficient adaptation methods (Freeze, P-tuning and LoRA), with domain knowledge incorporated via tailored prompt templates to enable intelligent knowledge extraction from CAD texts. Experimental results demonstrate that the domain-enhanced LLM achieves F1 scores of 81.64% on NER and 88.30% on RE — improvements of 12.76% and 3.97%, respectively, over conventional pretrained language models (PLMs). These results confirm the effectiveness of the proposed knowledge-embedded LLM framework in constructing aircraft fault KGs and advancing expert systems for civil aviation safety and airworthiness management.

As the rapid development of deep learning methods, neural machine translation (NMT) has attracted more and more attention in recent years. However, lack of bilingual resources decreases the performance of the low-resource NMT model seriously. To overcome this problem, several studies put their efforts on knowledge transfer from high-resource language pairs to low-resource language pairs. However, these methods usually focus on one single granularity of language and the parameter sharing among different granularities in NMT is not well studied. In this article, we propose to improve the parameter sharing in low-resource NMT by introducing multi-granularity knowledge such as word, phrase and sentence. This knowledge can be monolingual and bilingual. We build the knowledge sharing model for low-resource NMT based on a multi-task learning framework, three auxiliary tasks such as syntax parsing, cross-lingual named entity recognition, and natural language generation are selected for the low-resource NMT. Experimental results show that the proposed method consistently outperforms six strong baseline systems on several low-resource language pairs.

Accurate target recognition of unmanned aerial vehicles (UAVs) in the intelligent warfare mode relies on a highly standardized UAV knowledge base, and thus it is crucial to construct a knowledge graph suitable for UAV multi-source information fusion. However, due to the lack of domain knowledge and the cumbersome and inefficient construction techniques, the intelligent construction approaches of knowledge graphs for UAVs are relatively backward. To this end, this paper proposes a framework for the construction and application of a standardized knowledge graph from large-scale UAV unstructured data. First, UAV concept classes and relations are defined to form specialized ontology, and UAV knowledge extraction triples are labeled. Then, a two-stage knowledge extraction model based on relational attention-based contextual semantic representation (UASR) is designed based on the characteristics of the UAV knowledge extraction corpus. The contextual semantic representation is then applied to the downstream task as a key feature through the Multilayer Perceptron (MLP) attention method, while the relation attention mechanism-based approach is used to calculate the relational-aware contextual representation in the subject–object entity extraction stage. Extensive experiments were carried out on the final annotated dataset, and the model F1 score reached 70.23%. Based on this, visual presentation is achieved based on the UAV knowledge graph, which lays the foundation for the back-end application of the UAV knowledge graph intelligent construction technology.

Yi Fan, Anthony Hunter. Findings of the Association for Computational Linguistics: ACL 2023. 2023.