VIS4ML: An Ontology for Visual Analytics Assisted Machine Learning
VIS4ML: An Ontology for Visual Analytics Assisted Machine Learning
复制标题
DOI：
10.1109/tvcg.2018.2864838
复制
发表时间：
2019-01-01
影响因子：
5.2
通讯作者：
Chen, Min
中科院分区：
文献类型：
--
作者：
Sacha, Dominik;Kraus, Matthias;Chen, Min
虽然许多VA工作流程使用机器学习模型来支持分析任务。VA工作流在理解和改进机器学习（ML）过程中变得越来越重要。在这篇论文中。我们提出了一个本体（VIS4ML）的VA的一个子区域，即“VA辅助ML”。VIS4ML的目的是描述和理解ML中使用的现有VA工作流程，并检测ML流程中的差距以及将高级VA技术引入此类流程的潜力。在生物学、医学和许多其他学科中，本体已被广泛用于绘制主题的范围。我们采用学术方法构建VIS4ML，包括规范，概念化，形式化，实现和验证的本体。特别是，我们重新解释了传统的VA管道，包括模型开发工作流程。我们引入必要的定义，规则，语法，和视觉符号制定VIS4ML和利用语义Web技术实现它的Web本体语言（OWL）。VIS4ML捕获了有关VA用于辅助ML的先前工作流程的高级知识。它与已建立的VA概念一致，并将继续沿着VA和ML的未来发展。虽然这个本体论是建立VA的理论基础的努力，它可以被实践者在现实世界的应用程序中使用，以优化模型开发工作流程，通过系统地检查可以由机器或人类能力带来的潜在好处。与此同时，VIS4ML是可扩展的，并将继续更新，以反映未来使用VA构建高质量数据分析模型或快速构建此类模型的进展。
While many VA workflows make use of machine-learned models to support analytical tasks. VA workflows have become increasingly important in understanding and improving Machine Learning (ML) processes. In this paper. we propose an ontology (VIS4ML) for a subarea of VA, namely "VA-assisted ML'. The purpose of VIS4ML is to describe and understand existing VA workflows used in ML as well as to detect gaps in ML processes and the potential of introducing advanced VA techniques to such processes. Ontologies have been widely used to map out the scope of a topic in biology, medicine, and many other disciplines. We adopt the scholarly methodologies for constructing VIS4ML, including the specification, conceptualization, formalization, implementation, and validation of ontologies. In particular, we reinterpret the traditional VA pipeline to encompass model-development workflows. We introduce necessary definitions, rules, syntaxes, and visual notations for formulating VIS4ML and make use of semantic web technologies for implementing it in the Web Ontology Language (OWL). VIS4ML captures the high-level knowledge about previous workflows where VA is used to assist in ML. It is consistent with the established VA concepts and will continue to evolve along with the future developments in VA and ML. While this ontology is an effort for building the theoretical foundation of VA, it can be used by practitioners in real-world applications to optimize model-development workflows by systematically examining the potential benefits that can be brought about by either machine or human capabilities. Meanwhile, VIS4ML is intended to be extensible and will continue to be updated to reflect future advancements in using VA for building high-quality data-analytical models or for building such models rapidly.