Ontology to Concept Semantic Network Conversion for Text Analysis
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Solution Overview
Problem
Current text analytics techniques face limitations in accurately matching user expectations due to automated methodologies, with clustering techniques producing irrelevant outputs, idea extraction failing to identify secondary interests, and semantic analysis being computationally intensive and non-scalable.
Innovation Solution
The use of ontologies and concept semantic networks (CSNs) to capture and represent user interests formally, allowing for the creation of machine-readable meta-data structures that can analyze textual content effectively, with CSNs enabling users to focus on concepts rather than keywords and allowing for multi-level definitions and complex constructs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If semantic and syntax analysis techniques are used to accurately capture themes and patterns, then measurement precision is improved, but device complexity and computational requirements increase significantly
Solution Approach 1:
The patent segments the text analysis process into distinct stages: preprocessing (tokenization, stopword removal), core analysis (term frequency calculation, concept identification), and post-processing (result formatting). This segmentation allows each stage to be optimized independently, reducing overall computational complexity while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary actions by pre-processing text data before main analysis - removing stopwords, punctuation, and performing basic tokenization. This preliminary cleaning reduces the complexity of subsequent analysis by eliminating irrelevant elements that would otherwise require complex handling.
2Productivity
If automated clustering techniques are used to identify themes, then productivity is improved, but manufacturing precision deteriorates due to assumption-based outputs
Solution Approach 1:
The patent incorporates feedback mechanisms where analysis results are continuously refined. The system calculates term frequencies, identifies candidate concepts, and iteratively improves theme identification by comparing against established ontologies and knowledge bases, ensuring high precision while maintaining automation.
Solution Approach 2:
The patent uses intermediaries such as ontologies, thesauri, and knowledge bases that mediate between raw text and final theme identification. These intermediaries provide structured semantic relationships that guide the automated analysis, improving precision without sacrificing productivity.
3Ease of operation
If idea extraction techniques are used to capture main ideas, then ease of operation is improved, but adaptability deteriorates as secondary interests cannot be identified
Solution Approach 1:
The patent implements dynamic analysis capabilities that can adapt to different user needs. The system can adjust its focus between main ideas and secondary interests by modifying parameter thresholds, weighting schemes, and analysis depth, allowing it to serve multiple purposes while maintaining ease of operation.
Solution Approach 2:
The patent creates a universal text analysis platform that can perform multiple functions: identifying main ideas, detecting secondary interests, extracting entities, and analyzing sentiments. This multi-functional system maintains simplicity through a unified interface while adapting to diverse analytical needs.
4Measurement precision
If semantic indexing with thesaurus look-up is used to extract semantic content, then measurement precision is improved, but productivity deteriorates due to high volume of results
Solution Approach 1:
The patent applies local quality by focusing semantic analysis on specific regions or contexts within the text rather than uniformly analyzing entire documents. This allows precise semantic extraction where needed while skipping areas where it would be redundant, improving scalability.
Solution Approach 2:
The patent uses partial action by selectively applying semantic indexing only to relevant portions of text identified through preliminary analysis. Instead of processing entire documents with full semantic indexing, it applies the technique partially to high-value segments, maintaining precision while improving productivity.
Data Source
AI summary
A method and computer program product for producing a concept semantic network (CSN) from an ontology. Each class, subclass, object, restriction, and property of the ontology is converted into a concept of the CSN. Each hierarchy in the ontology is converted to a concept hierarchy of the CSN.