Ontology-Based Word Cloud for Multilingual Data Analysis
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Solution Overview
Problem
Existing automated data processing systems face challenges in efficiently interpreting and summarizing vast amounts of communication data, such as customer service interactions, across multiple languages and linguistic variations, leading to difficulties in extracting meaningful insights and sentiments.
Innovation Solution
The use of ontology programming with machine learning methods to develop a formal representation of concepts and their relationships, which adapts to specific domains by self-training on communication data, enabling the creation of word clouds that summarize key phrases, terms, and themes, and their frequency and origin, providing a user-friendly and comprehensive view of interaction data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional automated data processing systems are used to interpret and summarize communication data, then the processing can be automated, but the systems fail to efficiently extract meaningful insights and sentiments across multiple languages and linguistic variations
Solution Approach 1:
The patent introduces an ontology as an intermediary layer between raw communication data and analysis results. The ontology provides structured conceptual frameworks that mediate the processing of multilingual and culturally diverse communication data, enabling automated systems to preserve meaningful insights and sentiments while maintaining automation efficiency.
Solution Approach 2:
The system dynamically adjusts processing parameters based on the specific communication data being analyzed. By changing parameters such as linguistic models, cultural context weights, and sentiment analysis thresholds according to the data characteristics, the system efficiently extracts meaningful insights while adapting to different languages and contexts.
2Loss of information
If vast amounts of communication data are processed in detail, then comprehensive analysis is achieved, but the time required to extract insights and present information increases significantly
Solution Approach 1:
The patent segments the vast communication data into manageable units organized by ontology categories and themes. This segmentation allows the system to process different segments in parallel and present results in a structured, easily digestible format, reducing the time required to extract and present comprehensive insights without losing analytical depth.
Solution Approach 2:
The system transforms the analysis from a single-dimensional detailed text review to a multi-dimensional view using word clouds, thematic categorizations, and visual representations. This dimensional transformation allows users to grasp comprehensive insights quickly through visual patterns and aggregated statistics rather than reading through vast amounts of detailed data.
3Loss of information
If complex communication data is analyzed in detail, then deep insights are obtained, but the user interface becomes difficult to navigate and understand
Solution Approach 1:
The patent employs word cloud visualizations and graphical representations as another dimension for data presentation. Complex communication data is transformed into visual formats where importance, frequency, and relationships are conveyed through size, position, and clustering, making deep insights accessible and easy to understand without requiring users to navigate through complex detailed data.
Solution Approach 2:
The system uses color coding and visual differentiation to represent different themes, sentiments, and categories in the communication data. This visual encoding allows users to quickly grasp complex insights through color patterns and visual cues, significantly improving the ease of understanding and navigating the analyzed data while preserving analytical depth.
Data Source
AI summary
Machine learning-based methods to improve the knowledge extraction process in a specific domain or business environment, and then provides that extracted knowledge in a word cloud user interface display capable of summarizing and conveying a vast amount of information to a user very quickly. 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. The developed ontology can be applied to process a dataset of communication information to create a word cloud that can provide a quick view into the content of the dataset, including information about the language used by participants in the communications, such as identifying for a user key phrases and terms, the frequency of those phrases, the originator of the terms of phrases, and the confidence levels of such identifications.