Word Recognition Using Ontologies to Resolve Ambiguities
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Solution Overview
Problem
Existing word recognition systems face challenges in accurately interpreting ambiguous language elements, particularly in verbal and written expressions, due to uncertainties in character values, leading to errors in transcription and interpretation.
Innovation Solution
The implementation of ontologies in word recognition processes, which utilize syntactic and semantic analysis to resolve ambiguities by defining relationships among language elements and selecting the most likely values based on context, thereby improving accuracy in converting ambiguous character strings to text.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional word recognition systems are used, then the process is simple and fast, but accuracy is poor due to ambiguous character values
Solution Approach 1:
The patent introduces an ontology as an intermediary layer between the character recognition system and the final interpretation. The ontology provides a structured knowledge base that mediates the disambiguation process by defining relationships among language elements, enabling the system to resolve ambiguous character values through contextual relationships rather than direct recognition
Solution Approach 2:
The patent adds a semantic dimension to the traditional character recognition process. By incorporating ontological relationships and semantic analysis, the system moves from a one-dimensional character matching approach to a multi-dimensional analysis that includes syntactic structure, semantic meaning, and contextual relationships
2Measurement precision
If ontologies are used to resolve ambiguities, then word recognition accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The ontology is pre-constructed and stored before the actual word recognition process. By preparing the knowledge base in advance with all relationships among language elements, the system avoids the need for complex real-time computations during recognition, reducing processing time while maintaining high accuracy
Solution Approach 2:
The patent extracts only the necessary ontological relationships relevant to the specific recognition task rather than processing the entire ontology. This selective approach reduces computational overhead by focusing only on the subset of knowledge needed to resolve the ambiguities in the given context
Data Source
AI summary
Systems, and associated apparatus, methods, or computer program products, may use ontologies to provide improved word recognition. The ontologies may be applied in word recognition processes to resolve ambiguities in language elements (e.g., words) where the values of some of the characters in the language elements are uncertain. Implementations of the method may use an ontology to resolve ambiguities in an input string of characters, for example. In some implementations, the input string may be received from a language conversion source such as, for example, an optical character recognition (OCR) device that generates a string of characters in electronic form from visible character images, or a voice recognition (VR) device that generates a string of characters in electronic form from speech input. Some implementations may process the generated character strings by using an ontology in combination with syntactic and/or grammatical analysis engines to further improve word recognition accuracy.