Representing Experts' Interpretive Trails with Hyperknowledge Specifications

Representing users' creative and interpretive processes may be useful to identify problems and solutions associated with interactive decision-making processes. Generally, these processes are related to the interaction of users with some multimedia content (text, video, audio, images, etc.) and structuring users' tacit and explicit knowledge. Tracking such process should generate a representation of users' trails in a linear construction of time. However, this representation is generally not well structured from a knowledge engineering perspective. Considering highly immersive environments with interaction through multiple modalities, tracking this knowledge becomes even more complex. On the one hand, cognitive agents have been increasingly used to support decision-making practices, which may involve knowledge-intensive activities and critical thinking. On the other hand, these systems may demand an overly complex design and implementation given the lack of knowledge representations capable of describing steps of creative processes, including rich relationships between symbolic and non-symbolic data. Inferring human interpretation and knowledge in such a representation in these processes are fundamental to improve the design of systems that support decision-making, as well as general systems. In this work, we propose applying high-level conceptual components in a knowledge representation to explicitly characterize users' interpretive trails. The solution aims at supporting the representation of the multimedia data and knowledge sources someone interacted with, along with the sequence of steps expressing their interpretation strategies.