Goal Trajectories for Knowledge Investigations

Humans seek to gain knowledge and structure data by many means including both bottom-up and top-down methods. But in most instances, people have a specific purpose to their activity with data that drives the process. They often have particular questions that need answering in support of some broader investigation. These questions often change as answers point in various directions during an investigation, whether the investigation is formal (e.g., scientific, legal, journalistic, or military) or simply an informal browsing of the internet. In this paper, we take a mixed-initiative approach to knowledge discovery, and we present a system called Kyudo that supports the process using a conversational case-based reasoning process. Cases in Kyudo are sequences of knowledge goals or questions that form arcs through a multidimensional knowledge space and that form the core activity in a dialogue between the user and system. As the system gains more experience and therefore more cases, it is able to detect similarity in knowledge goals and prompt the user with additional relevant goals that can short circuit the human reasoning process to minimize tangents or false starts. In this paper we present a distance-based mechanism that reduces the total length of a goal trajectory through guidance that accelerates the human reasoning process and aids effective knowledge discovery.