Extraction and processing of procedural experiential knowledge in workflows - quality, interactivity, and transferability

本项目的目标是研究基于案例推理的新方法，以及在互联网社区中提取、表示和处理过程性经验知识的相关领域。程序性知识是在执行某项任务的过程中运用的知识。与计划类似，它描述了如何通过一系列步骤来做某件事或实现某一目标。在互联网论坛中，可以找到这种程序性经验知识的大量实例记录。在这个项目的第一个资助期进行的工作中，我们专注于过程性经验知识的工作流表示。我们的主要研究内容是从互联网社区的文本源中提取工作流，为用户的特定目标进行基于相似度的工作流检索，以及对检索到的工作流进行自动调整。有了这个建议，我们申请第二个资金期，在四个方面延长我们的工作：适应质量：虽然在我们之前的研究中，我们开发了几种方法，通过使用从工作流库中自动学习的适应知识来实现工作流的自动适应，但调整后的工作流的质量很难控制。因此，我们的目标是研究新的方法来评估自动适应的工作流的质量，以及评估学习的适应知识对工作流质量的影响的方法。互动性：到目前为止开发的工作流检索和适应方法都是全自动的，并假设了一个完全开发的查询，这对用户来说很难预先指定。因此，我们的目标是开发新的会话CBR方法，使之能够完全交互地解决涉及工作流检索和适配的问题。迁移学习：到目前为止研究的适配方法需要大量现有的过程性知识才能学习足够的适配知识。这使得很难处理程序性知识仍然稀少的小领域或新出现的领域。因此，我们的目标是研究迁移学习方法是否可以通过从具有大量程序性知识的不同但相关的领域转移知识来改善适应知识的学习。探索新的应用领域：到目前为止，我们主要在烹饪工作流领域演示了我们的方法。在第二个资助期，我们的目标是通过探索现有存储库集合中可用的工作流和业务流程模型存储库来扩大整个项目的实验基础，这些存储库集合最近在业务流程研究中可用。我们将选择几个知识库，并半自动地将它们转换为语义工作流或过程模型的案例库，作为计划中的实验工作的参考数据集。

The goal of this project is to investigate new methods in Case-Based Reasoning and related fields for extracting, representing and processing procedural experiential knowledge in Internet communities. Procedural knowledge is the knowledge exercised in the performance of some task. Similar to a plan, it describes how to do a certain thing or to achieve a certain goal through a sequence of steps. In Internet forums numerous records of instances of such procedural experiential knowledge is found. In the work performed during the first funding period of this project, we focus on workflow representations of procedural experiential knowledge. Our main research addressed the extraction of workflows from textual sources in Internet Communities, the similarity-based retrieval of workflows for a particular goal of a user, and the automatic adaptation of retrieved workflows. With this proposal, we apply for a second funding period to extend our work in four respects:Adaptation Quality: While in our previous research, we developed several methods that enable the automatic adaptation of workflows by using adaptation knowledge automatically learned from workflow repositories, the quality of the adapted workflows is difficult to control. Therefore, we aim at investigating new methods for assessing the quality of automatically adapted workflows as well as methods to assess the impact of the learned adaptation knowledge on the workflow quality.Interactivity: The workflow retrieval and adaptation methods developed so far are fully automatic and assume a fully developed query, which is difficult for a user to specify upfront. Therefore, we aim at developing new methods for conversational CBR that enable fully interactive problem solving involving retrieval and adaptation of workflows.Transfer Learning: The adaptation methods investigated so far require existing procedural knowledge of significant volume in order to learn enough adaptation knowledge. This makes it difficult to address small or newly emerging domains in which procedural knowledge is still sparse. Therefore, we aim at investigating whether transfer learning methods can be used to improve learning of adaptation knowledge by transferring knowledge from a different, but related domain with substantial procedural knowledge.Exploring New Application Domains: So far, we demonstrated our methods primarily in the domain of cooking workflows. In the second funding period, we aim at broadening the experimental basis for the whole project by exploring workflow and business process model repositories available in existing repository collections that become (recently) available within business process research. We will select several repositories and transform them semi-automatically into a case bases of semantic workflows or process models to be used as reference data set for the planned experimental work.