In the field of robotic control, the interdisciplinary and complex nature of knowledge leads to issues such as knowledge fragmentation and a steep learning curve, posing significant challenges to mastering domain expertise. Recommendation systems, as typical information-filtering tools, are often employed to facilitate knowledge retrieval. Nevertheless, they inherently suffer from cold-start and data sparsity problems, which compromise recommendation accuracy. To overcome these limitations, this study first combines the recommendation system with the knowledge graph and improves upon the traditional BERT-BiLSTM-CRF entity recognition model by incorporating an attention mechanism to enhance entity extraction performance. Verified on the public dataset, the accuracy rate, recall rate and F1 value of the improved model were 91.6%, 94.5% and 93.1%, respectively, which increased by 1.4%, 2.9% and 2.3%, respectively, compared with the BERT-BiLSTM-CRF model. Subsequently, by using the YOLOv5 object detection network to extract image features from the self-built robot image dataset, a multimodal robot control knowledge graph was constructed. Finally, to address the limitations of conventional KGAT, a Personalized Knowledge Graph Attention Network (PKGAT) model is proposed by integrating the recommendation system with the constructed knowledge graph and incorporating a cold-start mitigation module. This results in a knowledge graph-based recommendation system tailored for robotic control domain knowledge. The results from the experiments show that the AUC value and F1 value of the PKGAT model in the MovieLens dataset are 94.56% and 94.20% respectively, which have increased by 2.35% and 2.07%, respectively, compared with the KGAT model. The AUC value and F1 value in the Book-Crossing dataset were 75.30% and 73.60%, respectively, which increased by 2.02% and 1.48%, respectively, compared with the KGAT model.

As a high-level reuse, software design reuse has received more and more attention because of its important impact on the subsequent development stages. Usually, the design models are typically represented as some graphs or diagrams, in which Unified Modeling Language (UML) class diagram is so widely used in software design that it has become the de facto standard. There has been some research on the reuse of UML class diagrams so far, mainly focusing on matching and retrieval. However, it is worth noting that there are many similar class diagrams modeling the same object or some related class diagrams modeling different aspects of the same object in the reuse repository. As a matter of fact, the primary step to achieve a high-quality reuse is to have high-quality software artifacts, so the well-designed UML class diagrams become a necessary resource for software design reuse. Therefore, it is necessary to integrate these class diagrams so that they have stronger modeling ability, and eliminating redundancy is another benefit of the integration. Up to now, there has been little discussion about the integration of class diagrams, so we propose an integration approach based on semantics and structure in this paper. The equivalent elements that can identify the semantically merged parts are defined, and the possible conflict items are listed from both semantic and structural aspects. The integration procedure composed of three stages is proposed, in which an approach combining semantic common class diagrams (SCCDs) and structural common graph sequence (SCGS) is combined to determine the merged parts, the integration issue of heterogeneous class diagrams is considered from the proposed abstract models, and the conflict resolution for each conflict item is described by examples. The experimental results show the effectiveness of our proposed integration approach.

Often most software development doesn’t start from scratch but applies previously developed artifacts. These reusable artifacts are involved in various phases of the software life cycle, ranging from requirements to maintenance. Software design as the high level of software development process has an important impact on the following stages, so its reuse is gaining more and more attention. Unified modeling language (UML) class diagram as a modeling tool has become a de facto standard of software design, and thus its reuse also becomes a concern accordingly. So far, the related research on the reuse of UML class diagrams has focused on matching and retrieval. As a large number of class diagrams enter the repository for reuse, classification has become an essential task. The classification is divided into unsupervised classification (also known as clustering) and supervised classification. In our previous work, we discussed the clustering of UML class diagrams. In this paper, we focus on only the supervised classification of UML class diagrams and propose a supervised classification method. A novel ensemble classifier F-KNB combining both dependent and independent construction ideas is built. The similarity of class diagrams is described, in which the semantic, structural and hybrid matching is defined, respectively. The extracted feature elements are used in base classifiers F-KNN and F-NBs that are constructed based on improved K-nearest neighbors (KNNs) and Naive Bayes (NB), respectively. A series of experimental results show that the proposed ensemble classifier F-KNB shows a good classification quality and efficiency under the condition of variable size and distribution of training samples.