A GNN-based Recommender System to Assist the Specification of Metamodels and Models
A GNN-based Recommender System to Assist the Specification of Metamodels and Models
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基于 GNN 的推荐系统辅助元模型和模型的规范
DOI：
10.1109/models50736.2021.00016
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Phuong T. Nguyen
中科院分区：
文献类型：
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作者：
Juri Di Rocco;Claudio Di Sipio;D. D. Ruscio;Phuong T. Nguyen
如今，虽然建模环境为用户提供了指定不同种类的工件的设施，例如，元模型、模型和转换，从以前的建模经验中学习并在建模任务中得到帮助的可能性在很大程度上仍然没有被探索。在本文中，我们提出了MORGAN，一个推荐系统的基础上的图形神经网络（GNN），以协助建模者在执行规范的元模型和模型。指定的（Meta）模型和训练数据通过利用自然语言处理（NLP）技术以基于图形的格式编码。之后，图核函数使用提取的图为建模者提供相关的建议，以完成部分指定的（Meta）模型。我们使用各种质量指标在真实世界的数据集上评估了MORGAN，即，精确度、召回率和F度量。实验结果是令人鼓舞的，并证明了我们的工具，以支持建模，同时指定元模型和模型的可行性。
Nowadays, while modeling environments provide users with facilities to specify different kinds of artifacts, e.g., metamodels, models, and transformations, the possibility of learning from previous modeling experiences and being assisted during modeling tasks remains largely unexplored. In this paper, we propose MORGAN, a recommender system based on a graph neural network (GNN) to assist modelers in performing the specification of metamodels and models. The (meta)model being specified, and the training data are encoded in a graph-based format by exploiting natural language processing (NLP) techniques. Afterward, a graph kernel function uses the extracted graphs to provide modelers with relevant recommendations to complete the partially specified (meta)models. We evaluated MORGAN on real-world datasets using various quality metrics, i.e., precision, recall, and F-measure. The experimental results are encouraging and demonstrate the feasibility of our tool to support modelers while specifying metamodels and models.