Web Page Classification Using Context Features
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Solution Overview
Problem
Current web page classification methods face challenges in accurately categorizing web pages due to the heterogeneity of data formats and the lack of consistent meta tags, leading to inefficient and resource-intensive manual classification processes.
Innovation Solution
A system and method for automatically generating context features using URL, title, and meta features, which are then classified using machine learning techniques, reducing the need for extensive document processing and improving classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual web page classification is performed, then classification accuracy can be maintained, but the process becomes time consuming and labor intensive
Solution Approach 1:
The system enables web pages to classify themselves automatically by extracting and analyzing their own metadata (title, description, keywords, URL) without requiring manual human intervention. The automated classification system processes pages independently, resolving the contradiction between maintaining accuracy and reducing time consumption.
2Measurement precision
If extensive document processing is performed to improve classification accuracy, then more features can be extracted, but computational resources and time are consumed
Solution Approach 1:
The system extracts only the essential metadata features (title, description, keywords, URL) that are most relevant for classification, rather than processing the entire web page content. This selective extraction approach maintains classification accuracy while significantly reducing computational resource consumption.
3Adaptability or versatility
If heterogeneous web page formats are processed uniformly, then all pages can be classified, but the classification process becomes complex
Solution Approach 1:
The system uses a universal metadata extraction approach that works across all web page formats (HTML, XML, plain text) by focusing on common metadata elements present in diverse formats. This universal method handles heterogeneous formats without requiring format-specific processing logic, thereby reducing system complexity.
Data Source
AI summary
Described herein are methods and systems implementing a web page classification system for automatically generating at least one context feature for a web page and classifying the web page based on the at least one context feature. In one implementation, a context feature generating module of the web page classification system is configured to automatically generate at least one context feature based on at least two of uniform resource locator (URL) features, title features, and meta tags features of a web page and a classifying module is configured to classify the web page based on the at least one context feature.