Ontology-Based Dialog Framework Bootstrapping
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Solution Overview
Problem
Existing dialog frameworks require manual intervention to customize state automata parameters, creating a bottleneck in instantiating a dialog framework for end-applications.
Innovation Solution
An ontology-based method for automatically bootstrapping state-based dialog systems by partitioning ontology and knowledge graphs into sub-graphs, generating structured queries, determining dialog intentions, and creating connecting dialog states to generate an automata dialog framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual intervention is used to custom-fill state automata parameters, then the dialog framework can be customized for end-applications, but the instantiation process becomes a major bottleneck
Solution Approach 1:
The system automatically generates state automata parameters by processing domain-specific documents and data without requiring manual intervention. The dialog framework self-configures by extracting entities, relationships, and dialogue flows from provided documentation, eliminating the manual customization bottleneck while maintaining application-specific adaptability
Solution Approach 2:
The system performs preliminary processing of domain documents to pre-extract and structure information into usable parameters before the dialog framework needs to be instantiated. By pre-processing documentation to identify entities, relationships, and dialogue patterns in advance, the system prepares all necessary components for rapid framework generation without manual intervention during instantiation
2Productivity
If automated methods are used to generate dialog frameworks, then productivity increases, but the complexity of the system architecture increases
Solution Approach 1:
The system divides the complex task of dialog framework generation into distinct modular stages: document processing, entity extraction, relationship identification, state automata parameter generation, and framework assembly. Each module handles a specific aspect of the process independently, making the overall complex system manageable and maintainable while achieving high automation productivity
Solution Approach 2:
The system introduces intermediate data structures and processing layers that bridge the gap between raw domain documents and the final dialog framework. These intermediaries include structured representations of entities, relationships, and dialogue patterns that facilitate automated transformation without requiring direct complex mappings, thereby managing system complexity while maintaining high automation capability
Data Source
AI summary
Methods, systems, and computer program products for bootstrapping of state-based dialog systems are provided herein. A computer-implemented method includes determining parameters for state automata by partitioning an ontology graph into sub-graphs and a knowledge graph into sub-graphs, wherein the ontology graph and the knowledge graph are based on a user question and domain knowledge pertaining to the user question; generating a structured query for each of the sub-graphs; determining intentions of a dialog pertaining to the at least one user question by translating each of the generated structured queries to a respective natural language query; creating one or more dialog states for each of the determined dialog intentions; creating one or more connecting dialog states between pairs of the created dialog states; and generating an automata dialog framework associated with the user question based on the created dialog states and the created connecting dialog states.