Following the successful use of Google Blockly to build OWL class expressions, the next Otter Project effort is to move forward with building all description logic with Blockly. The outcome will be a full implementation of the edit functionality needed to educate your digital twin to perform in the digital ecosystem.
Knowledge vs Learning
The first step is to formalize the definition of knowledge building for AI. For the Otter Project, this definition has been extrapolated from the Knowledge Building theory attributed to Carl Bereiter and Marlene Scardamalia, two professors from the University of Toronto. Although they address knowledge building from the perspective of how human beings build their knowledge, their model is also applicable to AI.
According to their theory, knowledge building in education is either done in belief mode or design mode. Belief mode is what we are told to believe, based upon publicly shared knowledge. Design mode incorporates actual experience that fosters improvement. Consider that in AI, the same distinction exists. Building OWL DL documents is based upon shared beliefs, while training neural networks is based upon design by incorporating actual experience.
OWL DL documents represent the capturing of knowledge by humans to share with computers. Neural networks are essentially trained by repetition. The methods applied for training continue to improve so a computer can learn from big data observations.
Cognitive Artifacts
Bereiter and Scardamalia describe knowledge building as a way to create new cognitive artifacts. They recognize the importance of the community in building public knowledge artifacts, and of individuals applying innovation to build new artifacts. In the OTTER project, this concept is formalized by layered groups of knowledge topics for OWL DL documents as listed below:
- Innovation – Unique and private topics building upon Business, Academia, and Universal topics.
- Business – Topics defined by the North American Industry Classification System (NAICS) and building upon Academia and Universal topics.
- Academia – Topics defined by the Classification of Instructional Programs (CIP) and building upon Universal topics.
- Universal – Common topics for classification, processing, and federation as provided by the Otter Server.
Each OWL DL is classified in one and only one of a given topic.
Knowledge Building Graph
The following is an initial implementation showing a knowledge building graph. The Pizza Stores DL topic from the Otter Server prototype is selected as the focus for the topic filter. The visible layer options of Innovation, Business, and Academia are set on where Universal is set off.
Setting Business and Academia off and setting Universal on shows the Universal topics applied in the Pizza Stores OWL DL document.
The Knowledge Building graph is dynamically created based upon the selection criteria and the dependency of a OWL DL document on another. Dependency is based upon the imports defined in each of the OWL DL documents.
Knowledge Building Artifacts
In the OTTER project, these artifacts exist within Lights, Camera, Action, and Perform. Each of these will implement Blockly edits to visualize, create, and update content artifacts. The list of items in each are an indication of what is to come.
 | Conceptual Classes Data Properties Object Properties |
 | Physical Patterns Things |
 | Dialog Request/Response Send/Receive Actors |
 | Perform Query Database Request a Response |
This is the beginning implementation of building AI knowledge by the Otter Project using Google Blockly. It is the next step towards achieving the education of your digital twin.