Knowledge Base Maintenance via Targeted Memory Probing
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Solution Overview
Problem
Conventional methods for maintaining a knowledge base are time-consuming and rely heavily on human expertise, as they require manual research and evaluation of vast amounts of unstructured text to identify relevant information for addition to the knowledge base.
Innovation Solution
A system utilizing a neural network and an editorial device that generates and maintains a knowledge base by receiving queries, processing text inputs, and providing proposed triples with evidence records, allowing users to select and validate the relevance of these records for updating the knowledge base.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual research and evaluation methods are used to maintain a knowledge base, then the accuracy and reliability of information can be ensured through human expertise, but the time consumption and labor requirements increase significantly
Solution Approach 1:
The patent introduces an automated information processing system that acts as an intermediary between raw text sources and the knowledge base. This system includes modules for automatic text processing, entity recognition, relationship extraction, and evidence tracking, which handle the preliminary work of information gathering and validation, allowing human experts to focus on higher-level review and decision-making tasks.
Solution Approach 2:
The maintenance process is divided into distinct automated stages: text collection, preprocessing, entity extraction, relationship identification, evidence preservation, and quality assessment. Each stage is handled by specialized computational modules that work sequentially, breaking down the complex task of knowledge base maintenance into manageable, automatable components while maintaining traceability of the information flow.
2Productivity
If automated processing methods are used to maintain a knowledge base, then the speed and coverage of information processing improve, but the accuracy and reliability may decrease without human verification
Solution Approach 1:
The system implements multi-level feedback mechanisms including automated quality metrics that evaluate extracted information against predefined criteria, confidence score thresholds that trigger review workflows, and tracking of evidence provenance that allows verification of automated decisions. Human experts receive structured feedback about automated processing results, enabling them to verify and correct information efficiently rather than reviewing everything from scratch.
3Manufacturing precision
If comprehensive evaluation of all found materials is performed manually, then the quality and relevance of added information can be ensured, but the complexity and resource requirements of the system increase
Solution Approach 1:
The system performs comprehensive automated evaluation on all candidate information but applies full human review only to cases that meet specific criteria such as low confidence scores, conflicting information, or high-importance domains. For routine, high-confidence extractions, the system accepts automated results with minimal human intervention, achieving high quality standards without requiring exhaustive manual review of every piece of information.
Data Source
AI summary
A system to maintain a knowledge base including a device to: (i) generate a first interface to: receive a query for transmission to a question-answer system and provide a response including one or more proposed triple in a list, (ii) after selection of a particular triple, generate a second interface to: provide at least one evidence record including a span of text in support of the particular triple, and provide one or more control element associated with each evidence record including at least one of: a first control element selectable to cite its corresponding evidence record and span of text as supporting the particular triple, or a second control element selectable to prevent its corresponding evidence record and span of text from being cited as supporting the particular triple, and (iii) generate a data structure, based on selections of the one or more control element, to update the knowledge base.