Self-learning Document Tagging via Taxonomy Adaptation
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Solution Overview
Problem
Existing information retrieval systems face challenges in maintaining relevant taxonomies for document tagging as they evolve over time, requiring significant time and resources to update tags and adapt to changing enterprise needs.
Innovation Solution
A self-learning and adaptable mechanism for tagging electronic documents that uses generic and internal taxonomies, with user feedback to adjust relevance scores and taxonomy hierarchies, leveraging term frequency inverse document frequency (TF-IDF) and word embedding clouds to rank tags and update taxonomies dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual taxonomy maintenance is performed to keep tags relevant, then tag accuracy is improved, but time consumption and resource requirements increase
Solution Approach 1:
The system automatically maintains taxonomies by processing enterprise documents and extracting tags without manual intervention. The taxonomy maintenance mechanism self-updates by analyzing document content, entity types, and user interactions, eliminating the need for manual taxonomy curation while keeping tags accurate and relevant.
Solution Approach 2:
The system incorporates user feedback loops where user interactions with tagged documents (such as search queries, document access patterns, and explicit feedback) are continuously analyzed. This feedback drives automatic taxonomy updates, allowing the system to adapt to changing enterprise needs and maintain tag accuracy over time without additional manual effort.
2Adaptability or versatility
If manual taxonomy updates are performed to adapt to evolving enterprise needs, then adaptability is improved, but resource requirements increase
Solution Approach 1:
The taxonomy maintenance mechanism automatically adapts to evolving enterprise needs by continuously analyzing enterprise documents, extracting new entity types, and updating taxonomy structures without manual intervention. The system self-monitors document patterns and autonomously updates taxonomies to reflect changing business contexts, technologies, and organizational structures.
Solution Approach 2:
The taxonomy is designed as a dynamic structure that can automatically evolve over time. The system implements continuous learning mechanisms that detect changes in document patterns, entity relationships, and user behaviors, automatically restructuring taxonomies to maintain relevance. This dynamic adaptation occurs through automated processes rather than static manual updates.
3Measurement precision
If comprehensive tag sets are generated to improve document categorization, then categorization accuracy is improved, but processing time increases
Solution Approach 1:
The system pre-processes and analyzes enterprise documents to extract entity types and relationships in advance, building a comprehensive tag set before actual categorization tasks. By performing entity extraction and tag generation preliminarily, the system creates ready-to-use tag sets that can be quickly applied during document categorization, reducing real-time processing requirements while maintaining comprehensive coverage.
Data Source
AI summary
Implementations include providing a first set of tags by processing a document using generic entity extraction based on one or more external taxonomies, providing a second set of tags by processing the electronic document using specific entity extraction based on internal taxonomies specific to the enterprise, determining a relevance score for each tag in the first set of tags, and the second set of tags, defining a set of tags including one or more tags of the first set of tags, and one or more tags of the second set of tags, tags of the set of tags being in rank order based on respective relevance scores, receiving user input to the set of tags, and performing one or more of adjusting a ranking of tags based on the user input, and editing at least one internal taxonomy of the one or more internal taxonomies based on the user feedback.