Adaptive Ontology Controller for Natural Language Query Processing
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Solution Overview
Problem
Current search engine technologies are limited in processing personal data and responding to natural language queries, failing to effectively leverage user data for meaningful interactions beyond keyword searches.
Innovation Solution
A system that utilizes an adaptive ontology controller and data extractor/correlator to process natural language queries by harvesting knowledge from personal and public data sources, converting unstructured data into structured data, and generating meaningful responses based on user inputs, including voice commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional keyword search functionality is used, then the system is simple and easy to operate, but it cannot effectively process natural language queries or leverage personal data for meaningful interactions
Solution Approach 1:
The system segments the complex natural language processing task into distinct functional modules: an adaptive ontology controller for semantic interpretation, a data extractor/correlator for knowledge harvesting, and a response generation component. This modular segmentation enables sophisticated NLP capabilities while managing system complexity through organized functional decomposition.
Solution Approach 2:
The adaptive ontology controller serves as an intermediary layer between the user's natural language query and the personal data cloud. It translates human language into structured semantic representations that can be effectively queried against personal data, bridging the gap between natural language input and data retrieval operations.
2Loss of information
If personal data is stored in unstructured format, then the system is easier to populate and maintain, but it cannot be effectively queried or processed for meaningful information retrieval
Solution Approach 1:
The data extractor/correlator performs preliminary actions by proactively harvesting knowledge from personal data sources and converting unstructured data into structured formats in advance. This preprocessing enables efficient querying and meaningful information retrieval without requiring complex real-time processing during user interactions.
Solution Approach 2:
The system replaces manual data structuring and organization with automated intelligent agents that can autonomously extract, correlate, and structure knowledge from unstructured personal data. This substitution of mechanical manual processes with automated AI-driven processes enables effective knowledge extraction while maintaining ease of data population.
3Measurement precision
If the system processes only basic keyword searches, then the processing speed is fast and energy consumption is low, but the quality and relevance of search results are limited
Solution Approach 1:
The system applies partial processing by focusing computational resources on the most relevant aspects of each query. The adaptive ontology controller selectively extracts and correlates only the necessary knowledge elements needed to answer the specific query, rather than processing all available data uniformly. This approach maintains high result accuracy while preserving query processing throughput.
Data Source
AI summary
A machine-implemented method for supporting a natural language user request against a user's personal data cloud can include a machine receiving the natural language user request from the user, determining a semantic interpretation of the natural language user request, querying a semantically-indexed, integrated knowledge store based on the semantic interpretation, and responding to the natural language user request by displaying results of the querying, wherein the results correspond to an item within the user's personal data cloud.