Abstract
Complex design tasks often require performing diverse actions in a specific order. To (semi-)autonomously accomplish these tasks, applications need to understand and learn a wide range of design procedures, i.e., Creative Procedural-Knowledge (CPK). Prior knowledge base construction and mining have not typically addressed the creative fields, such as design and arts. In this paper, we formalize an ontology of CPK using five components: goal, workflow, action, command and usage; and extract components' values from online design tutorials. We scraped 19.6K tutorial-related webpages and built a web application for professional designers to identify and summarize CPK components. The annotated dataset consists of 819 unique commands, 47,491 actions, and 2,022 workflows and goals. Based on this dataset, we propose a general CPK extraction pipeline and demonstrate that existing text classification and sequence-to-sequence models are limited in identifying, predicting and summarizing complex operations described in heterogeneous styles. Through quantitative and qualitative error analysis, we discuss CPK extraction challenges that need to be addressed by future research.
Abstract (translated)
复杂的设计任务通常需要按照特定的顺序执行不同的操作。为了（半）自主地完成这些任务，应用程序需要理解和学习广泛的设计过程，即创造性程序知识（CPK）。先前的知识库建设和挖掘通常没有涉及到创意领域，如设计和艺术。在本文中，我们使用五个组件（目标、工作流、操作、命令和用法）将CPK的本体形式化，并从在线设计教程中提取组件的值。我们搜集了19.6K教程相关网页，并为专业设计师构建了一个Web应用程序，以识别和总结CPK组件。带注释的数据集由819个唯一命令、47491个操作和2022个工作流和目标组成。基于此数据集，我们提出了一个通用的CPK提取管道，并证明现有的文本分类和序列到序列模型在识别、预测和总结异构样式中描述的复杂操作方面是有限的。通过定量和定性的误差分析，讨论了CPK提取技术在未来研究中需要解决的问题。
URL
https://arxiv.org/abs/1904.08587
