Domain Model Construction via Conversational Agent Dialogs
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Solution Overview
Problem
Current methods for learning domain knowledge are laborious and limited, particularly when dealing with structured data, as they rely on predefined schemes and struggle to capture diverse and ever-changing user needs, and feature extraction from unstructured data is challenging due to noise and bias.
Innovation Solution
The approach involves using conversational agents to elicit domain knowledge through strategized dialog interactions, which allows for the construction, update, and enhancement of feature spaces by directly querying users and learning from their preferences, relationships, and experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If predefined schemes are used for structured data, then data processing is simplified, but the system cannot capture diverse and ever-changing user needs
Solution Approach 1:
The system enables users to self-define domain concepts and relationships through natural language dialogues. Users actively participate in shaping the domain model by providing examples, corrections, and refinements, making the system adapt to their specific needs without requiring complex pre-configured schemes.
Solution Approach 2:
The domain model is dynamically updated through iterative dialog interactions. The system evolves from initial generic concepts to customized domain-specific models by continuously incorporating user feedback, allowing the schema to adapt its structure and semantics based on actual user requirements.
2Adaptability or versatility
If feature extraction is performed on unstructured data, then diverse user needs can be captured, but noise and bias make the process challenging
Solution Approach 1:
The system incorporates multiple rounds of feedback where users review and correct extracted features. The dialog process allows users to clarify ambiguous information, correct misinterpretations, and guide the extraction process, thereby improving the accuracy of features derived from unstructured data.
Solution Approach 2:
Before extracting features from unstructured data, the system performs preliminary dialogue to establish context, define domain concepts, and pre-process the data. This preparation step reduces noise and bias by aligning the extraction process with the user's intended domain framework.
3Reliability
If domain knowledge is elicited through dialog interactions, then user preferences and relationships can be captured, but the process is laborious
Solution Approach 1:
The dialog system serves multiple functions simultaneously: it elicits domain concepts, extracts features, validates relationships, and refines the domain model. By consolidating these tasks into a single interactive process, the system reduces overall time investment compared to separate manual processes while maintaining high accuracy.
4Adaptability or versatility
If conversational agents are used to elicit domain knowledge, then feature spaces can be constructed and updated, but the system complexity increases
Solution Approach 1:
The conversational agent acts as an intermediary between the user and the domain model construction process. It translates natural language interactions into structured domain concepts and relationships, simplifying the overall system architecture by providing a user-friendly interface layer that handles the complexity of knowledge representation automatically.
Data Source
AI summary
Embodiments for building domain models from dialog interactions by a processor. A domain knowledge may be elicited from one or more dialog interactions with one or more users according to one or more dialog strategies. One or more domain models may be built and/or enhanced according to the domain knowledge.