Chatbot AI Schema Topology Mapping for Intent Entity Classification
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Solution Overview
Problem
Existing chatbot platforms face challenges in efficiently training AI systems with intents, entities, and dialogs, as the identification, classification, and generation of these primary building blocks are time-consuming and resource-intensive, and require frequent revisions.
Innovation Solution
A system comprising a natural language manager, relationship manager, and topology manager that processes semantically enriched documents to generate a cache of tokens, classify them, map relationships, and construct a topology, which is then used to create an AI schema for real-time communication flow in a chatbot platform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional chatbot training methods are used to identify, classify, and generate intents, entities, and dialogs, then the chatbot can understand conversations, but the process is time-consuming and resource-intensive requiring frequent revisions
Solution Approach 1:
The system performs preliminary classification of tokens into intents and entities by analyzing the training data structure beforehand. The topology manager pre-establishes the hierarchical relationships and communication flow patterns before actual chatbot deployment, reducing the need for frequent revisions during operation.
Solution Approach 2:
The system creates a topological representation that copies and simplifies the complex relationships between intents, entities, and dialogs. This topological model serves as a reusable template that can be rapidly instantiated and modified, eliminating the need to reprocess entire training datasets during revisions.
2Reliability
If detailed classification of tokens into intents and entities is performed, then the AI schema accurately represents conversation flow, but the processing complexity increases
Solution Approach 1:
The system segments the complex token classification process into distinct modules: the NL manager handles initial token classification, the relationship manager manages entity-intent mappings, and the topology manager constructs hierarchical representations. This segmentation reduces processing complexity by distributing tasks across specialized components.
Solution Approach 2:
The topology manager transforms the flat classification data into a multi-dimensional hierarchical topological structure that represents conversation flow. This dimensional transformation organizes intents, entities, and dialogs in a structured hierarchy, making the complex relationships more manageable and interpretable without losing accuracy.
3Productivity
If a topological structure is constructed to represent communication flow, then the AI schema efficiently supports real-time chatbot operations, but the initial construction process becomes more complex
Solution Approach 1:
The topological structure is constructed in advance during the schema generation phase, before real-time chatbot operations begin. The topology manager pre-establishes the hierarchical relationships, node connections, and communication flow paths, so that during real-time operations, the chatbot can efficiently traverse the pre-built topology without undergoing complex construction processes.
Data Source
AI summary
Embodiments relate to an intelligent computer platform to support a chatbot platform. A semantically enriched document is subjected to natural language processing to generate a cache of tokens, and further classify the tokens, including noun and verb tokens. For each verb token, a corresponding intent is generated, and for each noun token a corresponding entity is generated. A relationship between the generated intents and entities is mapped, and a topology representing the mapped relationship is constructed. A primary verb is identified and assigned as a root node in the topology, and an arrangement of entities related to primary verb are identified and assigned as child nodes related to the root node. The constructed topology is consumed to an AI schema for implementation in the chatbot platform to support real-time communication flow.