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

本计画的目标是探讨案例推理及相关领域的新方法，以撷取、表示及处理网际网路社群中的程序经验知识。程序性知识是指在完成某项任务时运用的知识。类似于计划，它描述了如何通过一系列步骤来做某件事或实现某个目标。在互联网论坛中，许多记录的实例，这样的程序经验knowledge.In本项目的第一个资金期间进行的工作中，我们专注于工作流程表示的程序经验知识。我们的主要研究解决了从互联网社区的文本源中提取工作流，基于相似性的检索工作流的特定目标的用户，和检索到的工作流的自动适应。 有了这个建议，我们申请第二个资助期，以扩大我们的工作在四个方面：适应质量：虽然在我们以前的研究中，我们开发了几种方法，使自动适应工作流程，通过使用自动适应知识从工作流程库，适应工作流程的质量是很难控制。因此，我们的目的是调查新的方法，用于评估自动适应的工作流的质量以及方法来评估的学习适应知识对工作流quality.Interactivity的影响：迄今为止开发的工作流检索和适应方法是全自动的，并假设一个完全开发的查询，这是很难为用户指定的前期。因此，我们的目标是开发新的对话CBR方法，使完全交互式的问题解决，涉及检索和适应workflows.Transfer学习：适应方法研究到目前为止，需要现有的程序知识的显着量，以学习足够的适应知识。这使得它很难解决小或新出现的领域，其中程序知识仍然稀少。因此，我们的目标是研究迁移学习方法是否可以用来提高适应知识的学习，通过从不同的，但相关的领域与大量的程序knowledge.Exploring新的应用领域：到目前为止，我们主要在烹饪工作流程的领域展示了我们的方法。在第二个资助期内，我们的目标是扩大整个项目的实验基础，探索工作流和业务流程模型存储库，在现有的存储库集合，成为（最近）在业务流程研究。我们将选择几个存储库，并将它们半自动地转换为语义工作流或过程模型的案例库，用作计划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.