Studying Interaction Patterns for Knowledge Graph Exploration

Abstract

The flexible data models of knowledge graphs (KGs) are powerful tools for handling large and dynamic data sets and are increasingly used for the tasks of data processing and storage. Although a KG may contain rich data and powerful connections, it is upon the searchers to explore these graphs and make sense out of them. The objective of this research paper is to investigate if and how KG exploration can be improved from a user’s point of view, to enhance the discovery of information. A qualitative user study should deliver insights on how different users interact with a KG, at what point they struggle and missed potential discoveries. Recognizing and understanding the intentions of the users is necessary to create solutions that support them best in their particular situation. Based on the findings, new features and improvements are suggested, developed and added to a prototypical KG exploration application, to be finally tested with regard to their impact on user exploration and acceptance. Based on the collected data we could identify the best guidance mechanisms that improve KG exploration the most.