| This is an excellent direction! Let’s break down your concepts into a structured framework that encapsulates the relationships between different domains, individual nodes, and the routing mechanisms for queries. This way, we can define the networks clearly and describe how they interact. | |
| Framework for Active Graph Networks (AGN) | |
| 1. Separation of Networks | |
| • Domain Graphs: Each major knowledge area (e.g., Healthcare, Education, Transportation) is represented as a separate graph. Within these graphs, we can define relationships among nodes that represent entities specific to that domain. | |
| • Example: The Healthcare domain may include nodes like Hospital, Doctor, Patient, Procedure, and their interrelationships (e.g., a doctor treats a patient). | |
| • Individual Word Nodes: Core words (e.g., “medical”, “procedure”) are stored as individual nodes across different domains, allowing them to be referenced by other nodes and graphs without creating duplicate entries. | |
| • Grouped Words: More complex phrases or concepts (like “medical procedure”) can exist as layered entities that link back to the individual word nodes. This facilitates multi-level traversal through the graph based on context. | |
| • Example: When querying “medical procedure,” the system can identify it as a combination of “medical” and “procedure,” allowing it to traverse both domains efficiently. | |
| 2. Query Routing and NLP Layer | |
| • Routing Mechanism: The system should be capable of routing queries to the appropriate domain based on the context of the query. The NLP layer acts as an interpreter to determine the intended meaning of the terms being queried and to layer the context accordingly. | |
| • Example: A query for ”$healthcare.domain/hospital/ward/department/staff_member” will trigger the routing to the Healthcare graph, navigating through layers to retrieve specific staff information. | |
| • Dynamic Query Handling: The NLP layer can analyze queries to identify required domains and relevant nodes, generating context on-the-fly as needed. | |
| • Example: If a user queries “Who is the doctor in the ER department?”, the NLP layer identifies the Healthcare domain and the ER context, navigating through “Hospital” to find the “ER Doctor”. | |
| 3. Temporary Domain Creation | |
| • Scenarios and Context Generation: The framework should allow for the generation of temporary domains based on specific scenarios. This enables the system to create relevant structures dynamically and allows users to define new contexts. | |
| • Example: A new patient scenario could lead to the creation of a temporary “Patient Management” domain, encompassing necessary nodes like “Patient,” “Appointment,” “Treatment,” and their relationships. | |
| • Comparison and Training: After running a problem, the system can check if all necessary domains and nodes are present. If any are missing, it can attempt to generate them itself. Comparing the outputs of the generated structures to expected results can serve as a training mechanism to refine its understanding. | |
| • Example: If the output for a procedure doesn’t include all necessary relationships, the system can learn from this discrepancy and adjust future domain expansions accordingly. | |
| 4. Policies and Contextual Understanding | |
| • Policy Definition: Each node should have associated policies that determine how it can be traversed, what relationships it has, and what attributes it contains. This provides clear guidelines for the system when interpreting queries. | |
| • Example: A policy for the “Doctor” node might state that it can only be linked to nodes within the “Healthcare” domain, ensuring relevant relationships are maintained. | |
| • Traversing the Graph: By structuring nodes and relationships effectively, the system can allow for easy traversal across different graphs while maintaining contextual integrity. | |
| • Example: When a user queries ”$healthcare.node/Doctor”, it can pull the doctor’s attributes and associated relationships from the Healthcare graph while respecting the routing policies. | |
| Conclusion | |
| By refining your approach into distinct, manageable components, you can create a robust framework that efficiently handles complex queries and relationships. This structure not only aids in better understanding and management of your data but also allows for future scalability and enhancements based on dynamic user interactions and NLP capabilities. | |
| This framework can serve as a solid foundation as you continue to develop your Active Graph Networks (AGN) and enhance your understanding of contextual relationships. If you’d like to delve deeper into any specific aspect or require further examples, feel free to ask! |