Ontology-Driven Knowledge Extraction Automation
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Solution Overview
Problem
Existing knowledge extraction methods require significant labor and time to adapt to changes in ontologies, especially when extracting knowledge from unstructured data in patent and academic papers.
Innovation Solution
A knowledge extraction apparatus and method that includes an ontology definition unit, a case generation unit, a sentence input unit, a prompt generation unit, a knowledge extraction control unit, a language model, and a knowledge verification unit, which together reduce the workload associated with ontology changes by automating the knowledge extraction process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a domain-specific natural language processing engine and ontology are used to extract knowledge automatically from literatures, then knowledge extraction automation is improved, but when the ontology changes, significant labor is required to update the natural language processing engine through addition and re-examination of rules or generation and re-training of supervised data
Solution Approach 1:
The system enables self-service by allowing the ontology to automatically update the natural language processing engine without requiring manual intervention. The ontology change unit detects ontology changes and automatically generates updated extraction rules or retraining data, making the system self-adapting to ontology evolution.
Solution Approach 2:
The system implements feedback mechanisms where the ontology change unit continuously monitors ontology changes and feeds this information back to the natural language processing engine. This feedback loop enables automatic adjustment of extraction rules and models based on ontology updates, eliminating the need for manual re-examination and retraining.
2Measurement precision
If labor is used to extract knowledge from literatures, then extraction accuracy can be maintained, but it takes considerable time and labor since knowledge is extracted by reading and interpreting the literatures
Solution Approach 1:
The patent introduces an intermediary system consisting of the ontology and natural language processing engine that mediates between the unstructured literature text and the structured knowledge extraction process. This intermediary automatically interprets and extracts knowledge according to the ontology definitions, maintaining accuracy while eliminating manual reading and interpretation work.
Solution Approach 2:
The system replaces the mechanical process of manual reading and interpretation with an automated natural language processing system. The NLP engine processes text mechanically and systematically according to predefined ontology rules, achieving both high accuracy and high productivity by substituting human cognitive labor with automated computational processes.
Data Source
AI summary
A sentence input unit that receives a case sentence that is a target of knowledge extraction from the outside and outputs the case sentence as target sentence data; a prompt generation unit that outputs a knowledge extraction prompt including ontology definition data, case data, the target sentence data, and a predetermined prompt template; a knowledge extraction control unit that accepts, as an input, the knowledge extraction prompt and knowledge extraction control setting data in which an extraction condition of the knowledge extraction is prescribed and outputs a knowledge extraction command to execute the knowledge extraction; a language model that accepts the knowledge extraction command as an input and outputs an extracted knowledge related to a knowledge extracted; and a knowledge verification unit that accepts the extracted knowledge as an input and verifies validity of the extracted knowledge are included.