Ontology Intelligent Construction Technology for NOTAM

Mi, Baigang; Fan, Yanping; Sun, Yu

doi:10.1109/icicip53388.2021.9642197

Cited by 1 publication

(1 citation statement)

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“…For aircraft maintenance faults, Wang et al [8] proposed an ontology for aircraft faults in order to solve the problem that multi-source aviation maintenance data lead to syntactic and semantic conflicts, thus limiting the integration and sharing of aircraft fault information, which provides a unified and specific description of multi-source data and eliminates the semantic heterogeneity. For aviation intelligence and operational safety, Mi et al [9] used natural language processing techniques and clustering algorithms to intelligently extract ontologies for navigational announcement information.…”

Section: Construction Of Uav Knowledge Graphmentioning

confidence: 99%

Research on the Intelligent Construction of UAV Knowledge Graph Based on Attentive Semantic Representation

Fan

Sun

et al. 2023

Drones

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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.

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Section: Construction Of Uav Knowledge Graphmentioning

confidence: 99%