Ontology Programming for Automated Theme Labeling
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Solution Overview
Problem
Automated interpretation of customer service interactions faces challenges in interpreting content and sentiments from human communication, particularly in automatically identifying meaningful terms and relations within communication data across various languages and domains.
Innovation Solution
The use of ontology programming with machine learning methods to develop a formal representation of concepts and their relationships, adapting to linguistic patterns and properties, and self-training to understand the domain, enabling the detection and classification of meaningful terms and their relations, which are then grouped into themes for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated interpretation methods are used to process communication data, then productivity is improved, but measurement precision deteriorates due to difficulty in accurately detecting and measuring meaningful terms and relations across multiple languages and domains
Solution Approach 1:
The system performs self-training by processing and analyzing a defined corpus of communication data to automatically learn domain-specific terminology, linguistic patterns, and relationships. This self-service mechanism enables the system to improve its own measurement precision without requiring manual reconfiguration for each new domain or language.
Solution Approach 2:
The ontology programming adapts to linguistic patterns and properties by adjusting its internal representations and classification criteria. The system modifies its parameter settings based on the specific domain and language being analyzed, allowing it to maintain high measurement precision across diverse communication data while preserving automated interpretation capabilities.
2Ease of operation
If ontology programming is used to classify and group terms into themes, then ease of operation is improved through user-friendly display, but device complexity increases due to the need for formal representation of concepts and relationships
Solution Approach 1:
The system segments the complex communication data into manageable themes by grouping related terms and relations together. Each theme represents a distinct concept or topic, breaking down the overall complexity into smaller, more manageable units that can be displayed and operated with ease while maintaining the underlying formal ontology structure.
Solution Approach 2:
Themes serve as an intermediary layer between the complex ontology structure and the user interface. This intermediary abstraction allows users to interact with simplified theme representations rather than the full complexity of the underlying ontology, reducing the perceived device complexity while maintaining operational ease.
3Adaptability or versatility
If machine learning-based self-training is applied to adapt to domain-specific language, then adaptability is improved, but loss of time increases during the training process
Solution Approach 1:
The system performs preliminary training by processing and analyzing a defined corpus of communication data to establish domain-specific terminology and relationships before actual use. This preliminary action enables the system to adapt quickly to new domains and languages during operation, reducing the time loss that would otherwise occur during on-site training.
Solution Approach 2:
The self-training mechanism incorporates feedback from the processed communication data to continuously refine and update the ontology representations. This feedback loop allows the system to adapt to domain-specific language patterns efficiently, improving adaptability while minimizing training time through iterative learning rather than exhaustive retraining.
Data Source
AI summary
The disclosed solution uses machine learning-based methods to improve the knowledge extraction process in a specific domain or business environment. By formulizing a specific company's internal knowledge and terminology, the ontology programming accounts for linguistic meaning to surface relevant and important content for analysis. For example, the disclosed ontology programming adapts to the language used in a specific domain, including linguistic patterns and properties, such as word order, relationships between terms, and syntactical variations. Based on the self-training mechanism developed by the inventors, the ontology programming automatically trains itself to understand the domain or environment of the communication data by processing and analyzing a defined corpus of communication data.