Hybrid URL Categorization with Semantic Feedback Loop
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Solution Overview
Problem
Rule-based URL categorization engines struggle with scalability and exhaustivity, as they can only categorize a limited subset of web pages and require manual upgrades to extend their coverage, failing to effectively analyze the diverse and ever-evolving landscape of web sites.
Innovation Solution
A system that combines real-time data collection from IP data traffic using monitoring probes, processing URLs through both rule-based and semantic-based categorization engines. The rule-based engine assigns categories from a pre-defined list, while the semantic engine performs analysis on textual content for uncategorized URLs, with matching categories being fed back to update the rule engine and prioritizing frequent uncategorized URLs for further analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a rule-based categorization engine is used to categorize URLs, then the categorization process is fast and efficient, but the coverage is limited and cannot handle the diverse and evolving landscape of web sites
Solution Approach 1:
The system segments the categorization task into two distinct engines: a rule-based engine for fast categorization of known URL patterns, and a semantic-based engine for comprehensive analysis of uncategorized URLs. This segmentation allows each engine to specialize - the rule-based engine maintains high speed for routine cases while the semantic-based engine provides exhaustive coverage for diverse and evolving web sites through NLP and machine learning techniques.
2Adaptability or versatility
If the rule-based categorization engine is extended to cover more web pages, then the coverage increases, but manual upgrades are required which reduce efficiency
Solution Approach 1:
The system implements a feedback mechanism where the semantic-based categorization engine analyzes uncategorized URLs and generates new rules that are automatically fed back to the rule-based categorization engine. This closed-loop feedback system enables the rule-based engine to automatically expand its coverage by learning from the semantic engine's analyses, eliminating the need for manual rule upgrades while maintaining high categorization efficiency.
Solution Approach 2:
The rule-based categorization engine enhances its own capabilities through self-service by automatically incorporating rules generated from the semantic-based engine's analysis results. This self-updating mechanism allows the system to autonomously expand its coverage to handle new and diverse web site patterns without requiring external manual intervention, thus maintaining productivity while improving adaptability.
3Adaptability or versatility
If all uncategorized URLs are analyzed by the semantic-based categorization engine, then exhaustive coverage is achieved, but the processing time and computational resources increase significantly
Solution Approach 1:
The system applies partial action by having the semantic-based categorization engine process only the subset of URLs that were not categorized by the rule-based engine. This selective approach ensures exhaustive coverage of uncategorized URLs while avoiding redundant processing of already-categorized URLs, thereby minimizing processing time and computational resource consumption while maintaining complete exhaustiveness.
Solution Approach 2:
The system segments the URL population into two groups: categorized URLs handled by the fast rule-based engine and uncategorized URLs requiring semantic analysis. This segmentation allows the computational intensive semantic-based engine to focus only on the necessary subset of URLs, achieving exhaustive coverage where needed while preserving overall processing efficiency by avoiding unnecessary analysis of already-handled URLs.
4Productivity
If a hybrid approach combining rule-based and semantic-based engines is used, then both efficiency and exhaustiveness are improved, but the system complexity increases
Solution Approach 1:
The hybrid system is segmented into two specialized engines with clearly defined responsibilities: the rule-based engine handles fast categorization of known patterns, while the semantic-based engine provides comprehensive analysis of uncategorized URLs. This functional segmentation manages system complexity by creating modular, independent components with well-defined interfaces, making the overall system easier to maintain and scale despite combining multiple approaches.
Solution Approach 2:
The system applies partial action by having the semantic-based engine operate only on the subset of URLs that escape the rule-based engine's categorization. This selective deployment reduces the computational burden and simplifies the system architecture compared to running a full semantic analysis on all URLs, thereby managing complexity while maintaining both efficiency and exhaustiveness where needed.
Data Source
AI summary
The present method and system relate to categorizing URLs (Uniform Resource Locators) of web pages accessed by multiple users over an IP (Internet Protocol) based data network. The method and system collect real time data from IP data traffic occurring on the IP based data network, and extract parameters from the collected real time data, the parameters including an URL of a web page. The URL is processed by a rule based categorization engine, to associate a matching category to the URL of the web page. When no matching category is inferred, the URL is transferred to a semantic based categorization engine. A matching category is associated to the transferred URL by the semantic based categorization engine, based on a semantic analysis of the textual content extracted from the web page associated to the URL.