Research On Knowledge Graph Technology Of Electromagnetic Spectrum Warfare

Electromagnetic spectrum doctrine is rich in content and concept,including overview,relationship,organization,planning and implementation of operations,which reflects the foreign military's consideration of operations.Therefore,based on electromagnetic spectrum doctrine,this paper uses knowledge graph to solve the problem of text discovery.The construction of knowledge graph is a systematic problem.Aiming at the unstructured text data,this paper uses the top-down method,combined with ontology modeling,natural language processing,knowledge representation learning and other technologies to construct the military doctrine knowledge graph.The innovative research work of this paper is as followsAccording to the content structure characteristics of electromagnetic spectrum warfare doctrine,this paper proposes a framework of electromagnetic spectrum warfare doctrine knowledge graph construction by integrating ontology construction,knowledge extraction and map completion technology,and constructs the knowledge graph based on this framework,which shows the practicability of this framework.Due to the need for standardized processing of text data,this paper,according to the description and definition in relevant regulations,combined with the domain background,extracts the characteristics of text description and entity relationship,defines 4 classes and 13 relationships for electromagnetic spectrum regulations,and constructs the ontology model of electromagnetic spectrum warfare.The model of word segmentation,part of speech tagging and dependency parsing is used to preprocess the data,and rules are used to extract entities and relationships.The traditional link prediction model constructs entity and relation vectors by minimizing the loss function.Although these methods have made progress in simple relations,they have some problems,such as simple measurement function,inapplicability of interval parameters,and lack of advantages in modeling complex relations.To solve the above problems,this paper proposes a adaptive embedding model.Different from the traditional model given global parameters,this paper weights the loss measure by operator and linearly correlates the interval,and realizes the self applicability of the model by determining the parameters in the training process.In this paper,the proposed model is verified in the electromagnetic spectrum regulations,and good results are achieved.In order to further verify the effectiveness of the model,experiments are carried out on open datasets wn18 and fb15 k.The results show that compared with the traditional transformation model,this model reduces the average ranking of data set wn18 by more than 120,and increases the proportion of correct entities by more than 2%;reduces the average ranking of data set fb15 k by more than30,and increases the proportion of correct entities by more than 6%.The results on public data sets also show that the model can better adapt to complex environment because of its adaptive characteristics.