Unmanned Aerial Vehicle Knowledge Graph Construction with SpERT

Wu, Yike; Zhu, Yingdi; Li, Jingchen; Zhang, Chengye; Gong, Tianling; Du, Xiyuan; Wu, Tianxing

doi:10.1007/978-981-19-0713-5_17

Cited by 2 publications

(1 citation statement)

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“…Currently, ontology construction usually adopts a seven-step approach [33], in which the core aspects include listing important terms in the domain, defining classes and their hierarchical relation, defining class attributes and their relation, etc. As for UAV ontology construction, it is more oriented to unstructured text data, and there is currently no ontology that can be directly reused to learn from.…”

Section: Uav Ontology Definitionmentioning

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: Uav Ontology Definitionmentioning

confidence: 99%