Explanation, Mechanistic Reasoning, and Abstraction: Hypertext and Hypermodels

Semantic Web Interest Group,

While recently exploring causal models and machine learning (see also: [1][2]), I had some thoughts about graph-based knowledge representations. These thoughts pertain to explanation, mechanistic reasoning, and abstraction. These thoughts also pertain to organizing and navigating spaces of related (causal) models.

One can consider the following, increasingly detailed, set of explanatory sentences.


  1.  The robot caused the elevator to arrive.
  2.  The robot pressed the button which caused the elevator to arrive.
  3.  The robot used its arm to press the button which caused the elevator to arrive.
  4.  The robot pressed the button which closed a circuit which sent electricity to a control system while simultaneously causing the button to light up. The elevator control system, having received the electric signal from the button press, dispatched an elevator to the floor that the robot was on.
  5.  The robot used its arm, hand, and finger to press the button which closed a circuit which sent electricity to a control system while simultaneously causing the button to light up. The elevator control system, having received the electric signal from the button press, dispatched an elevator to the floor that the robot was on.

When considering these sentences from a larger space of (graph of) explanatory sentences, from a simple explanation through increasingly detailed sentences, one can also consider that each sentence can be described as mapping to a graph-based knowledge representation of that sentence’s semantics. In this case, each sentence can also be described as mapping to a (causal) model.

One could use hypertext to provide a “hyper-explanation” such that users could click on components of explanatory sentences, their phrases or lexemes, to navigate to other explanations having a greater level of detail with respect to the clicked-on contents. One could also use context menus to navigate. Sentences’ phrases and lexemes could each be expanded in a number of ways.

Similarly, as these sentences each map to (graph-based) diagrams, one could use “hypermodels” or “hyperdiagrams” to provide users with the capability of clicking on various visual diagram components to navigate through increasingly detailed models or diagrams. One could, similarly, use context menus to navigate these spaces. Graph nodes and edges (and perhaps subgraphs) could each be expanded in a number of ways.

Users could navigate through spaces of (graphs of) explanatory sentences and/or spaces of (graphs of) diagrammatic (causal) models by interacting with hypertext representations and/or by interacting with “hypermodel” or “hyperdiagram” representations.

Thank you. I hope that these ideas are of some interest. Any thoughts on these topics?


Best regards,
Adam Sobieski
http://www.phoster.com

[1] https://www.microsoft.com/en-us/research/video/panel-challenges-and-opportunities-of-causality/
[2] https://crossminds.ai/video/yoshua-bengio-towards-causal-representation-learning-603e9c53706789c68965058c/