Conversational Agent for Information Model Authoring
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Solution Overview
Problem
Expert programmers are often required to author information models, leading to domain experts needing additional training or increased project resources, resulting in higher costs, delayed schedules, and potential miscommunication due to lack of domain knowledge in data storage or processing technologies.
Innovation Solution
A conversational agent system that enables domain experts to author information models through natural language interactions, using a robust natural language processor for precise and predictable results, with modules for understanding, managing, and generating information models, allowing for validation and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If expert database programmers manually author information models, then the information models can be accurately structured, but domain experts need additional training or more project members, which drives up business costs and delays the project schedule
Solution Approach 1:
The system enables domain experts to author information models independently through natural language conversation, without requiring training in database programming. The conversational agent guides experts through the modeling process, allowing them to self-author accurate information models using their domain knowledge rather than requiring specialized technical training.
Solution Approach 2:
The conversational agent acts as an intermediary between domain experts and the information model authoring process. It translates natural language inputs from domain experts into structured information models, bridging the gap between domain knowledge and technical modeling requirements without requiring experts to learn complex programming languages.
2Reliability
If expert database programmers manually author information models, then the technical structure can be ensured, but more project member positions are needed, which drive up business costs
Solution Approach 1:
Domain experts can author information models independently using natural language conversation, eliminating the need for specialized database programmer positions. The system enables self-service authoring where experts leverage their existing domain knowledge without requiring additional technical expertise or hiring specialized personnel.
Solution Approach 2:
The conversational agent serves as an intermediary that handles the technical translation work between domain experts and the information model structure. This mediator absorbs the complexity of technical modeling, allowing domain experts to focus on their expertise areas without needing to hire specialized programming personnel.
3Ease of manufacture
If expert database programmers manually author information models, then the data storage structure can be optimized, but miscommunication may occur due to lack of domain knowledge in the technology
Solution Approach 1:
The conversational agent acts as a mediator that translates domain expert terminology into technically accurate information model structures. This intermediary ensures that domain concepts are accurately captured without loss or miscommunication, while still producing optimized data storage structures through the system's underlying technical capabilities.
Data Source
AI summary
A method and a system for interpreting conversational authoring of information models. The system includes an understanding module, a managing module, and a generating module. The understanding module is configured to understand a natural language input to interpret an output. The managing module is configured to construct an information model based on the output of the understanding module. The generating module configured is to prompt, as a response to the natural language inputs, wherein the natural language inputs determine concepts and relationships of the concepts. The method includes receiving an interactive dialog between a conversational agent and an information model designer in natural language to produce an information model. The method can further include validating the information model using an information model management system. The method can include interpreting the information model with the use of an application.