Multi-Dimensional Text Classifier with Ontology for Intelligent Decision Support

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Solution Overview

Problem

Current text classifiers are limited in making intelligent decisions as they rely on predefined categories and cannot predict user intentions or provide feedback to aid in decision-making.

Innovation Solution

Integration of a text classifier with a knowledge domain ontology and multi-dimensional classifiers that use semantic standards to classify text, allowing for intelligent decision-making by fusing semantic attributes and querying a domain ontology knowledge library for decision derivation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If predefined categories are used for text classification, then classification efficiency is improved, but decision-making intelligence deteriorates

Engineering Contradiction:

Improveclassification efficiencyVSAvoiddecision-making intelligence

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The patent introduces a knowledge domain ontology as an intermediary between the text classifier and the decision-making process. The ontology serves as a mediator that transforms predefined classification categories into meaningful decision support information by establishing semantic relationships and domain-specific knowledge contexts, enabling intelligent decisions while maintaining classification efficiency

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent extends the classification system from a single-dimensional predefined category approach to a multi-dimensional framework that incorporates semantic standards, knowledge domain ontology, and confidence rates. This dimensional expansion allows the system to operate at multiple levels of abstraction, transforming simple categorization into comprehensive decision support while preserving efficiency through automated processing

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

2Speed

If text is classified using traditional classifiers, then processing speed is improved, but the ability to predict user intention deteriorates

Engineering Contradiction:

Improveprocessing speedVSAvoiduser intention prediction accuracy

Core Design Contradiction:

SpeedVSMeasurement precision

Solution Approach 1:

The system performs preliminary actions by pre-processing text through feature extraction and model training before actual classification. The multi-dimensional classifier is pre-trained on semantic standards and knowledge domain ontology, enabling it to quickly classify new text while accurately predicting user intentions based on pre-established semantic relationships and domain knowledge

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces traditional mechanical classification mechanisms with a semantic-based intelligent system. Instead of relying solely on keyword matching and predefined categories, the system uses semantic analysis, knowledge domain ontology, and confidence rate calculations to predict user intentions, achieving both speed and precision through automated semantic processing

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If multiple classifiers with different semantic standards are integrated, then decision-making capability is improved, but system complexity increases

Engineering Contradiction:

Improvedecision-making capabilityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent segments the decision-making system into distinct functional modules: multi-dimensional classifiers for different semantic standards, a knowledge domain ontology module, and a decision support module. This segmentation allows each component to specialize in specific tasks while working together, improving decision-making capability while managing complexity through modular architecture

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent creates a universal framework where multiple classifiers with different semantic standards can be integrated and reused across different decision-making scenarios. The knowledge domain ontology serves as a universal knowledge base that supports all classifiers, enabling the system to handle diverse text classification tasks and decision support requirements through a single unified platform

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS8868468B2Intelligent decision supporting system and method for making intelligent decisions and classifying text using classifiers trained to define different semantic standards

Publication Date: 2014.10.21 SAMSUNG ELECTRONICS CO LTD

AI summary

An intelligent decision system and a method for making an intelligent decision are provided. The intelligent decision supporting system includes a multi-dimensional classifier comprising a plurality of classifiers that define different semantic standards and are trained based on the different semantic standards, for classifying a text by the semantic standards and for outputting a plurality of attributes of the text and a confidence rate of each of the plurality of attributes, a question submitting module for receiving the output of the multi-dimensional classifier, for forming a question based on the plurality of attributes of the text and the confidence rate of each attribute, and for submitting the question to an inference machine, the inference machine for receiving the question submitted by the question submitting module, for inquiring of a domain ontology knowledge library based on the question, and for providing an answer for the question to an decision reply module.