Semantic Attribution for User Input Interpretation
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Solution Overview
Problem
Self-service applications in data processing systems face challenges in understanding and mapping user inputs, as users often describe real-world needs in free-form text that do not align with formal categories, leading to ineffective validation and response generation.
Innovation Solution
A method for semantic attribution that involves a processor receiving source data statements, selecting a domain, and semantically analyzing the statements using an ontology to match elements and assign annotations, thereby bridging the gap between user terminology and system-centric categorization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If formal categories are used to describe domain concepts, then system validation and response generation become more reliable, but user input interpretation becomes less accurate because users describe needs in free-form text that does not align with formal categories
Solution Approach 1:
The patent introduces an intermediary component (semantic analyzer/ontology mapping module) that translates between user's free-form text and the system's formal categories. This intermediary performs semantic analysis to bridge the gap between natural language expressions and structured domain concepts, allowing both user-friendly input and system-reliable validation to coexist.
Solution Approach 2:
The system dynamically changes the parameter of category representation from rigid formal definitions to flexible semantic representations. By using ontology-based semantic analysis, the system can interpret multiple natural language expressions that map to the same formal category, thereby improving interpretation accuracy while maintaining validation reliability through the underlying formal structure.
2Ease of manufacture
If system-centric terminology is used in user interface, then system validation becomes easier, but user understanding and input alignment become more difficult
Solution Approach 1:
Instead of requiring users to adapt to system-centric terminology, the patent inverts the approach by having the system adapt to user's natural language. The semantic analyzer processes user inputs in their original terminology and automatically maps them to system categories, eliminating the need for users to learn system-specific jargon while maintaining validation effectiveness.
Solution Approach 2:
The ontology-based semantic analysis system serves multiple functions: it acts as a natural language processor, a category mapper, and a validation engine simultaneously. This universal component handles both user-friendly interpretation and system-reliable validation in a single processing pipeline, improving ease of operation without sacrificing validation ease.
3Measurement precision
If semantic analysis using ontology is implemented, then user input interpretation accuracy improves, but system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-building and storing domain ontologies and semantic relationships in a knowledge base before runtime. This preprocessing step creates reusable semantic models that can be quickly queried during user interactions, avoiding the need for complex real-time semantic reasoning and reducing operational system complexity while maintaining high interpretation accuracy.
Solution Approach 2:
The system uses copying by creating simplified semantic representations (annotated elements with assigned ontology entries) of complex user inputs. Instead of maintaining full semantic analysis complexity throughout the system, it creates lightweight copies or annotations that capture essential meaning, reducing downstream processing complexity while preserving interpretation accuracy.
Data Source
AI summary
Elements in source data statements are matched to respective one or more entries in an ontology associated with a domain that had been selected for the source data statements. The source data statements had been received for a request. The ontology includes items and relationships that define the selected domain. The one or more entries are assigned to the matched elements, respectively, to annotate each matched element with a respective annotation consisting of the respective one or more entries. The annotated elements are saved with the respective annotations. The annotations are used to generate a search query for the request.