"The proposed approach is based on 3 key steps. First, derive knowledge from different sources of information (experimental data, expert learning, machine learning). Secondly, unify the different knowledge representations. Finally, organise the unified knowledge in a way that captures its generalisation hierarchy and facilitates the design of efficient prediction algorithms (Figure 3)." page 2 of 21.
Straight away this figure in Hanser et al. (2014) suggested learning and learners and the four care domains of Hodges' model. There are profound differences of course.
The information at the start takes the form of concepts, data, but 'information' also applies. The context in combining chemistry and informatics is radically different when it comes to who, or what is learning in that first arrow. The paper's keywords are:
Machine learning Knowledge discovery Data mining SAR QSAR SOHN Interpretable model Confidence metric and Hypothesis Network.
To a healthcare or social care student the knowledge encountered may well be raw. The principle of Hodges' model encourages the generation of hypotheses within and then across the model's knowledge domains. The learn-er can then attempt to unify these thoughts and reflections, organising them in a variety of ways. Hodges' model is not itself in self-organising, but there is a pattern-learning process for the learner and a pattern-matching process for the expert or specialist.
Hanser, T., Barber, C., Rosser, E., Vessey, J., Webb, D., & Werner, S. (2014). Self organising hypothesis networks: A new approach for representing and structuring SAR knowledge. Journal of Cheminformatics, 6(1), 1-21.