Email triage with Mind Meshes

M. Harbach, T. Muders, Ralf Groeper, Matthew Smith
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Abstract

While efficient email triage is a well-known problem, the currently used email filtering solutions are still rudimentary and machine centric. Most filtering solutions work best if humans add machine interpretable keywords to the subject line of emails. More natural filtering options, such as project membership lists or content-based filtering, are very time consuming to set up and maintain. Especially in research ecosystems, significant fluctuation in team members, organisations and projects themselves is common. Research projects are also usually managed in an ad-hoc manner, making static filtering rules a sub-optimal solution. In this paper we therefore introduce the Mind Mesh paradigm, a novel human-friendly information management approach. Based on a self-describing graph structure, organisational structures, projects, and collaborations are modelled. Leveraging this information, the system can automate and support administrative processes such as email filtering, based on an understanding of the project structures. It relieves users of the necessity to maintain machine interpretable rules by hand and adapts to new situations by itself.
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使用Mind Meshes进行邮件分类
虽然有效的电子邮件分类是一个众所周知的问题,但目前使用的电子邮件过滤解决方案仍然是初级的,并且以机器为中心。如果人类在邮件主题行添加机器可解释的关键字,大多数过滤解决方案都能发挥最佳效果。更自然的过滤选项,如项目成员列表或基于内容的过滤,设置和维护都非常耗时。特别是在研究生态系统中,团队成员、组织和项目本身的显著波动是很常见的。研究项目通常也以一种特别的方式进行管理,使得静态过滤规则成为次优解决方案。因此,在本文中,我们介绍了一种新的对人类友好的信息管理方法——Mind Mesh范式。基于自描述图结构,对组织结构、项目和协作进行建模。利用这些信息,系统可以基于对项目结构的理解,自动化并支持诸如电子邮件过滤之类的管理过程。它减轻了用户手工维护机器可解释规则的必要性,并能自行适应新情况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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