Ontology-Based Semantic Rendering for Ambiguity Reduction
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Solution Overview
Problem
Current devices render natural language strings without transformative conversion, leading to inaccuracies due to biases, missing values, and ambiguity, and struggle with structured text that requires specific vocabularies and context-specific rules, making knowledge sharing and project organization complex and ambiguous.
Innovation Solution
A modular system for ontology-based semantic rendering that retrieves and executes scripts based on ontology information within and outside documents, using a structured ontology to organize project knowledge and render content according to context, reducing ambiguity and improving knowledge sharing and project management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If natural language processing is used to render content, then transformation capability is improved, but accuracy deteriorates due to biases, missing values, and ambiguity
Solution Approach 1:
The patent introduces an intermediary ontology layer between natural language processing and content rendering. This ontology serves as a structured intermediary that mediates between the ambiguity of natural language and the precision requirements of rendering, resolving contradictions by providing a standardized representation layer that reduces biases and missing values while maintaining transformation capability.
Solution Approach 2:
The patent segments the content rendering process into distinct modules: natural language processing, ontology mapping, and rendering execution. This segmentation allows each component to optimize independently - the NLP handles transformation while the ontology ensures accuracy through structured representation, thereby resolving the accuracy-transformation contradiction.
2Loss of information
If structured text with specific vocabularies is used, then knowledge sharing is improved, but device complexity increases
Solution Approach 1:
The patent creates a universal ontology framework that can be applied across multiple domains and devices. This multi-functional ontology serves both structured text processing and knowledge sharing purposes, reducing the need for domain-specific complex systems while maintaining high knowledge sharing quality through standardized vocabularies.
Solution Approach 2:
The patent uses ontology as a standardized template or copy that can be reused across different content types and devices. Instead of creating complex custom systems for each use case, the ontology provides a reusable framework that simplifies device complexity while preserving knowledge sharing effectiveness through consistent structured representation.
3Measurement precision
If context-specific rules are applied, then rendering accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary action by pre-establishing the ontology framework and vocabularies before actual rendering occurs. This allows context-specific rules to be pre-organized and indexed within the ontology structure, enabling fast retrieval and application during rendering without the time cost of constructing complex rules ad hoc, thus maintaining high accuracy while reducing processing time.
Data Source
AI summary
Ontology-based semantic rendering is performed by retrieving a rendering script from a location represented by an ontology reference of a concept of content included in a document, the document further including an ontology dataset identifier of the concept and the ontology reference, and rendering the concept of content according to the rendering script and a mode of rendering represented by a rendering device.