Intelligent Modeling Method for OV Models in DoDAF2.0 Based on Knowledge Graph

Architecture framework has become an effective method recently to describe the system of systems (SoS) architecture, such as the United States (US) Department of Defense Architecture Framework Version 2.0 (DoDAF2.0). As a viewpoint in DoDAF2.0, the operational viewpoint (OV) describes operational activities, nodes, and resource flows. The OV models are important for SoS architecture development. However, as the SoS complexity increases, constructing OV models with traditional methods exposes shortcomings, such as inefficient data collection and low modeling standards. Therefore, we propose an intelligent modeling method for five OV models, including operational resource flow OV-2, organizational relationships OV-4, operational activity hierarchy OV-5a, operational activities model OV-5b, and operational activity sequences OV-6c. The main idea of the method is to extract OV architecture data from text and generate interoperable OV models. First, we construct the OV meta model based on the DoDAF2.0 meta model (DM2). Second, OV architecture named entities is recognized from text based on the bidirectional long short-term memory and conditional random field (BiLSTM-CRF) model. And OV architecture relationships are collected with relationship extraction rules. Finally, we define the generation rules for OV models and develop an OV modeling tool. We use unmanned surface vehicles (USV) swarm target defense SoS architecture as a case to verify the feasibility and effectiveness of the intelligent modeling method.