Taxonomy-Based Object Classification via Search String Scoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Classifying large amounts of data according to a taxonomy is tedious, time-consuming, and error-prone, especially when documents need to be continuously re-classified and new documents are added, as it requires analyzing content and identifying relevant nodes in a hierarchical structure.
Innovation Solution
A computer-implemented method that uses search strings formed by concatenating class keywords associated with nodes in a taxonomy to classify objects by performing searches and identifying the classes with the highest search scores, allowing for efficient and automatic classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification methods are used to analyze document content and identify taxonomy nodes, then classification accuracy can be maintained, but the process becomes tedious, time-consuming, and error-prone when dealing with large amounts of data
Solution Approach 1:
The patent replaces manual mechanical classification processes with an automated search engine-based system. Search engines execute queries against document content using taxonomy keywords, automatically retrieving and classifying documents without human intervention. This substitution maintains classification accuracy while dramatically reducing time consumption and eliminating human error in large-scale data processing
Solution Approach 2:
The classification system performs self-service by automatically generating search queries from taxonomy nodes, executing searches against document collections, and assigning classifications based on search results. The system autonomously handles the entire classification workflow without requiring manual analysis, enabling efficient processing of large datasets while maintaining consistent accuracy
2Quantity of substance
If manual classification is performed on millions of documents in corporate or academic intranets, then comprehensive classification coverage can be achieved, but the complexity and resource requirements increase significantly
Solution Approach 1:
The patent leverages the universal capabilities of search engines to perform classification across diverse document types and taxonomy structures. The same search engine infrastructure handles everything from small intranet collections to millions of documents, eliminating the need for separate classification systems. This multi-functionality reduces overall system complexity while enabling comprehensive classification coverage across large quantities of documents
3Reliability
If continuous re-classification and addition of new documents are required, then data currency is maintained, but the time and computational resources required increase substantially
Solution Approach 1:
The patent implements continuous classification by having the system automatically and repeatedly execute search queries against new and updated documents. Rather than periodic batch processing, the search engine continuously retrieves documents matching taxonomy keywords and updates classifications in real-time. This continuous action maintains data currency while preserving high productivity through automated operation
Solution Approach 2:
The system performs preliminary indexing and search query preparation in advance, so that when new documents are added or existing documents are updated, the classification process can immediately execute without delay. Search queries are pre-formulated from taxonomy nodes, and the search engine is ready to process documents as they become available, maintaining currency without sacrificing throughput
Data Source
AI summary
Objects, such as documents, are classified according to a taxonomy. The taxonomy includes nodes, corresponding to object classes, arranged in a hierarchy. Class keywords are associated with the nodes. Search strings are formed for the classes by traversing the taxonomic branches and concatenating the keywords associated with the classes. For each object to be classified, a search engine is used to perform searches on the object using the search strings. The searches produce search scores for each search string. Each object is classified by identifying the class(es) corresponding to the highest search score(s) for the object, and classifying the object into the identified class(es).