Recent advances in semantic web have shown how entity related searches have benefited from entity-based knowledge graphs. However, much of the commonsense knowledge about the real world is in the form of procedures or sequences of actions. Also, search log analysis shows that ‘how-to queries’ make up a significant amount of users’ queries. Unfortunately, these kinds of knowledge are missing from most knowledge graphs and commonsense knowledge bases in use. To empower semantic search, and other intelligent applications, computers need a much broader understanding of the world properties of everyday objects, human activities, and more. Luckily, such knowledge is abundantly available on-line and can be accessed from how-to communities. One domain of interest by on-line communities is the health domain, whereby users usually seek home remedies to common health-related issues. An example of such queries might be ‘how to stop nausea using acupressure’ or ‘how to aid digestion naturally’. To answer such questions, we need systems that understand natural language and knowledge bases with task frames of solutions in a holistic approach, including the tools required, the agents involved, and the temporal order of the actions. Our goal is to construct a machine-readable domain targeted high precision procedural knowledge base containing task frames. We developed a pipeline of methods leveraging open information extraction tool to extract procedural knowledge by tapping into on-line communities. Also, we devised a mechanism to canonicalize the task frames into clusters based on the similarity of the problems they intend to solve. The resulting know-how knowledge base, HealthAidKB, consists of more than 71 K task frames which are structured hierarchically and categorically; and can be used in many applications such as semantic search, digital personal assistants, human-computer dialog and computer vision. A comprehensive evaluation of our knowledge base shows high accuracy.

语义网的最新进展表明，与实体相关的搜索如何从基于实体的知识图中受益。但是，有关现实世界的许多常识性知识都是程序或动作序列的形式。此外，搜索日志分析表明，“操作方法查询”占了用户查询的大部分。不幸的是，大多数使用的知识图谱和常识知识库都缺少这些知识。为了支持语义搜索和其他智能应用程序，计算机需要对日常对象，人类活动等的世界特性有更广泛的了解。幸运的是，此类知识可在网上大量获取，并可从操作社区获取。在线社区感兴趣的领域之一是卫生领域，使用者通常会针对常见的健康问题寻求家庭疗法。这样的查询的一个例子可能是“如何使用指压法来阻止恶心”或“如何自然地帮助消化”。为了回答这些问题，我们需要一种能够以一种整体的方法来理解自然语言和知识库以及解决方案任务框架的系统，其中包括所需的工具，所涉及的主体以及动作的时间顺序。我们的目标是构建一个包含任务框架的针对机器可读域的高精度过程知识库。我们开发了一系列方法，这些方法利用开放信息提取工具通过利用在线社区来提取过程知识。此外，我们设计了一种机制，可根据任务框架要解决的问题的相似性将任务框架规范化为集群。HealthAidKB，由超过71 K的任务帧组成，这些任务帧按层次结构进行分类；并可以用于许多应用程序，例如语义搜索，数字个人助理，人机对话和计算机视觉。对我们的知识库的综合评估显示出很高的准确性。