Pattern Recognition for Web Search Result Extraction
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Solution Overview
Problem
Federated search engines face challenges in effectively extracting and aggregating data from web search engine result pages, particularly due to the presence of noisy markup tags and varying formats, which hinders the efficient dissociation of relevant information.
Innovation Solution
A computerized method and system for pattern recognition in web search engine result pages that simplifies HTML by removing noisy tags, identifies repeating patterns, evaluates them based on predefined criteria, and generates a regular expression to extract specific fields, enabling efficient data extraction and aggregation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If regular expressions are used to extract data from search engine result pages, then data extraction capability is improved, but the complexity of handling noisy markup tags and varying formats increases
Solution Approach 1:
The system performs preliminary actions by automatically generating regular expressions through pattern recognition before actual data extraction occurs. The algorithm analyzes result pages, identifies repeating patterns in data fields, and creates optimized regular expressions in advance, eliminating the need for manual pattern creation and reducing the complexity of handling varying formats.
Solution Approach 2:
The system enables self-service by allowing the pattern recognition algorithm to automatically generate and refine regular expressions without human intervention. The algorithm evaluates multiple patterns, selects the highest-rated ones based on predefined criteria, and produces ready-to-use regular expressions that adapt to different search engine result page formats.
2Measurement precision
If manual pattern creation is used for data extraction, then accuracy of field extraction is improved, but time consumption and productivity are reduced
Solution Approach 1:
The system enables self-service by allowing the pattern recognition algorithm to automatically generate and refine regular expressions without human intervention. The algorithm evaluates multiple patterns, selects the highest-rated ones based on predefined criteria, and produces ready-to-use regular expressions that adapt to different search engine result page formats.
Solution Approach 2:
The system uses copying by identifying repeating patterns across multiple result pages and replicating successful extraction patterns. The algorithm analyzes numerous examples of data fields, copies the underlying structural patterns, and generates regular expressions that can be applied consistently across different search engines and query types.
3Adaptability or versatility
If federated search engines aggregate results from multiple sources, then search coverage is improved, but the difficulty of extracting and standardizing data from different formats increases
Solution Approach 1:
The system applies universality by creating a multi-functional pattern recognition algorithm that can handle multiple search engine formats simultaneously. The generated regular expressions are designed to work across different data structures and markup styles, enabling the federated search engine to aggregate results from diverse sources using a unified extraction approach.
Solution Approach 2:
The system uses parameter changes by dynamically adjusting the regular expression patterns based on the specific characteristics of each search engine result page. The algorithm modifies pattern parameters such as delimiters, field separators, and matching criteria to adapt to varying formats while maintaining consistent data extraction across different sources.
Data Source
AI summary
Described herein are methods and systems for pattern recognition in web search engine result pages. The input data is a result page from a web search engine as well as an integer number for the results on the page. The output is a regular expression that matches all the results on the page, capturing each result and its individual fields.