Unsupervised URL Mining via Entity Extraction
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Solution Overview
Problem
Existing methods for enhancing network resource references, such as URLs, in local search systems face challenges with inadequate coverage and precision, leading to high resource costs and lack of scalability.
Innovation Solution
An unsupervised machine learning algorithm and distributed scraping method are employed to filter and select resource references by extracting entity information from local search data, constructing queries, and parsing search engine responses to identify relevant URLs based on confidence, popularity, and similarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to collect resource references, then coverage may be maintained at basic levels, but precision and relevance of URLs are inadequate
Solution Approach 1:
The URL mining system is divided into distinct functional modules: entity information extraction component, query construction component, distributed scraping component, and URL mining component. Each module performs a specific task in the pipeline, allowing the system to achieve high precision through specialized processing at each stage while maintaining manageable complexity through modular design.
Solution Approach 2:
Entity information (such as business names, categories, and attributes) serves as an intermediary between the search query and the final URL selection. The system extracts entity information from structured data, uses it to construct targeted queries, and leverages the results to identify relevant URLs, thereby improving precision through this intermediate representation layer.
2Quantity of substance
If comprehensive scraping is performed across multiple search engines, then coverage of resource references increases, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary extraction of entity information from structured local search data before conducting the scraping operation. By pre-processing and organizing entity attributes (names, categories, locations) into a ready-to-query format, the system enables faster query construction and execution across multiple search engines, thereby maintaining high processing speed while achieving comprehensive coverage.
Solution Approach 2:
The distributed scraping component queries multiple search engines in a systematic, periodic manner rather than attempting simultaneous comprehensive scraping. This structured approach allows the system to distribute the scraping load across different engines and time intervals, improving processing efficiency while still achieving broad coverage of resource references through the coordinated queries.
3Measurement precision
If manual filtering and selection of URLs is performed, then precision may be improved, but processing speed and scalability are reduced
Solution Approach 1:
The URL mining component automatically scores and ranks candidate URLs based on their relevance to the queried entity, using algorithms that evaluate factors such as entity-URL matching quality and domain authority. This self-service automated selection process eliminates the need for manual filtering while maintaining high precision, thereby significantly reducing the time required for URL selection and improving scalability.
Solution Approach 2:
The system transforms the URL selection problem from a manual qualitative assessment into an automated quantitative scoring process. By defining specific parameters for evaluation (such as entity-URL similarity scores, domain reputation metrics, and relevance weights), the system enables rapid automated filtering that maintains precision while dramatically reducing processing time compared to manual methods.
Data Source
AI summary
Architecture for deep mining of network resource references such as URLs. The architecture includes an extraction component configured to extract useful entity information from a collection of entity information, the collection of entity information derived from local search data; a distributed processing component configured to distributively query a search engine using the useful entity information and receive search results from the search engine, the search results comprising resource references; and, a selection component configured to remove non-relevant resource references to obtain candidate resource references and select a top resource reference from the candidate resource references, using an unsupervised machine learning algorithm.