Towards a Structural Framework for Explicit Domain Knowledge in Visual Analytics

TL;DR

This paper proposes a generalized structural framework for explicit domain knowledge in visual analytics, addressing current gaps by integrating linked data models and demonstrating applicability in healthcare physiotherapy data analysis.

Contribution

It introduces a novel structural framework for explicit domain knowledge in visual analytics, based on linked data models, and demonstrates its application in healthcare data analysis.

Findings

Framework addresses desiderata for explicit domain knowledge

Model leverages linked data for better integration

Demonstrated in physiotherapy visual analytics environment

Abstract

Clinicians and other analysts working with healthcare data are in need for better support to cope with large and complex data. While an increasing number of visual analytics environments integrates explicit domain knowledge as a means to deliver a precise representation of the available data, theoretical work so far has focused on the role of knowledge in the visual analytics process. There has been little discussion about how such explicit domain knowledge can be structured in a generalized framework. This paper collects desiderata for such a structural framework, proposes how to address these desiderata based on the model of linked data, and demonstrates the applicability in a visual analytics environment for physiotherapy.