Ontology Programming for Communication Data Analysis
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Solution Overview
Problem
Current automated data processing systems face challenges in interpreting and analyzing communication data, such as customer service interactions, due to the complexity of linguistic meanings and variations, which hinders effective knowledge extraction and analysis.
Innovation Solution
The implementation of ontology programming using machine learning methods that adapt to specific domains by identifying and classifying meaningful terms, forming relations, and grouping them into themes, allowing for a compressed view of interaction characteristics through self-training on a defined corpus of communication data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional automated data processing systems are used to analyze communication data, then processing speed is maintained, but knowledge extraction accuracy deteriorates due to inability to interpret linguistic meanings and variations
Solution Approach 1:
The patent introduces an intermediary ontology layer between raw communication data and analysis results. The ontology serves as a mediator that translates linguistic variations into standardized concepts, enabling accurate knowledge extraction without requiring the entire system to become overly complex. This intermediary structure resolves the contradiction by providing semantic interpretation capability while maintaining manageable system architecture.
Solution Approach 2:
The system employs self-training mechanisms where the ontology automatically learns from communication data corpus. The machine learning component enables the system to self-improve its understanding of domain-specific language patterns, reducing the need for manual programming of linguistic rules. This self-service approach improves extraction accuracy while avoiding the complexity of hand-crafted linguistic processors.
2Measurement precision
If ontology programming with machine learning is implemented to improve knowledge extraction, then analysis accuracy is improved, but processing time increases due to self-training requirements
Solution Approach 1:
The patent performs preliminary ontology training and development before actual analysis tasks. The self-training process is executed in advance on a defined corpus of communication data, creating a pre-optimized ontology structure. This preliminary action allows the system to achieve high analysis accuracy during actual processing without the time cost of training during real-time analysis, effectively resolving the time-accuracy tradeoff.
Solution Approach 2:
The system creates a simplified representation (copy) of the complex communication data structure through the ontology. By copying and transforming the data into standardized ontological concepts, the system can perform accurate analysis more efficiently. This copying process separates the complexity of raw data from the analysis operation, reducing processing time while maintaining accuracy through the structured representation.
3Ease of operation
If themes are named based on the three most common relations to provide compressed view, then user interface friendliness is improved, but information completeness may deteriorate
Solution Approach 1:
The patent segments theme naming into hierarchical levels: primary names based on the three most common relations for user-friendly display, and secondary detailed identifiers that preserve complete information. This segmentation allows the user interface to present simplified, friendly theme names while maintaining access to complete information through drill-down capabilities. The segmentation resolves the contradiction by separating display simplicity from data completeness.
Solution Approach 2:
The system adds an additional dimension to theme representation by incorporating both condensed names (for display) and expanded identifiers (for data integrity). This dimensional approach allows the same theme to be represented at different levels of detail depending on the operational context. The user sees friendly condensed names in the interface, but complete information is preserved in the underlying data structure, resolving the completeness-simplicity tradeoff.
Data Source
AI summary
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. The ontology is built on the premise that meaningful terms are detected in the corpus and then classified according to specific semantic concepts, or entities. Once the main terms are defined, direct relations or linkages can be formed between these terms and their associated entities. Then, the relations are grouped into themes, which are groups or abstracts that contain synonymous relations. 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. 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.