Learning and understanding user interface semantics from heterogeneous networks with multimodal and positional attributes

• Main Authors: ANG, Meng Kiat Gary, LIM, Ee-peng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Computing methodologies; Neural networks; Human-centered computing; User interface management systems; Artificial intelligence; Information systems; Multimedia information systems; Artificial Intelligence and Robotics; Databases and Information Systems; OS and Networks
• Online Access: https://ink.library.smu.edu.sg/sis_research/8323; https://ink.library.smu.edu.sg/context/sis_research/article/9326/viewcontent/3578522__1_.pdf
• Institution: Singapore Management University

User interfaces (UI) of desktop, web, and mobile applications involve a hierarchy of objects (e.g., applications, screens, view class, and other types of design objects) with multimodal (e.g., textual and visual) and positional (e.g., spatial location, sequence order, and hierarchy level) attributes. We can therefore represent a set of application UIs as a heterogeneous network with multimodal and positional attributes. Such a network not only represents how users understand the visual layout of UIs but also influences how users would interact with applications through these UIs. To model the UI semantics well for different UI annotation, search, and evaluation tasks, this article proposes the novel Heterogeneous Attention-based Multimodal Positional (HAMP) graph neural network model. HAMP combines graph neural networks with the scaled dot-product attention used in transformers to learn the embeddings of heterogeneous nodes and associated multimodal and positional attributes in a unified manner. HAMP is evaluated with classification and regression tasks conducted on three distinct real-world datasets. Our experiments demonstrate that HAMP significantly outperforms other state-of-the-art models on such tasks. To further provide interpretations of the contribution of heterogeneous network information for understanding the relationships between the UI structure and prediction tasks, we propose Adaptive HAMP (AHAMP), which adaptively learns the importance of different edgeslinking different UI objects. Our experiments demonstrate AHAMP’ssuperior performance over HAMP on a number of tasks, and its ability to provide interpretations of the contribution of multimodal and positional attributes, as well as heterogeneous network information to different tasks.