Web Crawler for Entity Location Data Verification
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Solution Overview
Problem
Existing methods for obtaining accurate structured location data for entities are inadequate, as manual entry is prone to errors and may not be comprehensive, leading to sub-optimal application performance and ineffective analytics.
Innovation Solution
A custom web crawler is used to extract and verify location information from entity websites, employing natural language processing and machine learning to identify reliable patterns and assign reliability scores, ensuring accurate and current data for application-specific functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual location data entry is used, then data collection is simple, but data accuracy and reliability deteriorate due to errors and outdated information
Solution Approach 1:
The system enables self-service by having entities proactively submit and update their own location data through online interfaces. This maintains ease of data collection while improving reliability through direct entity input, reducing manual errors and ensuring entities can update their own information when addresses change or they dissolve.
Solution Approach 2:
The system implements feedback mechanisms where location data is continuously verified against multiple sources including entity websites, social media profiles, and third-party databases. Discrepancies trigger alerts for manual review, creating a feedback loop that maintains data accuracy while allowing automated collection processes to operate.
2Reliability
If opt-in location data submission is used, then data privacy is protected, but data comprehensiveness deteriorates as many entities do not register
Solution Approach 1:
The system uses an intermediary approach by automatically collecting location data from publicly available online sources such as entity websites, social media profiles, and third-party databases. This intermediary data collection method expands data comprehensiveness beyond registered entities while maintaining privacy protection through automated, transparent data gathering from public sources.
Solution Approach 2:
The system implements multi-functionality by combining multiple data collection methods: opt-in submission from registered entities, automated extraction from public online sources, and verification against third-party databases. This universal approach ensures both privacy-protecting opt-in mechanisms and comprehensive data coverage from unregistered entities coexist.
3Quantity of substance
If automated web crawling is used to extract location data, then data comprehensiveness improves, but system complexity and processing requirements increase
Solution Approach 1:
The automated data collection system is segmented into specialized modules: web crawlers for extracting location data from entity websites, social media scrapers for alternative data sources, verification systems comparing data against third-party databases, and quality assessment components. This segmentation manages system complexity by dividing the comprehensive data collection task into independent, maintainable components.
Solution Approach 2:
The system performs preliminary actions by pre-processing and validating location data during extraction from multiple sources. Data is verified against entity websites, social media profiles, and third-party databases before being integrated into the main database, reducing downstream processing complexity and ensuring data quality upfront.
4Reliability
If multiple data sources are verified for location accuracy, then data reliability improves, but processing time and computational resources increase
Solution Approach 1:
The system applies partial verification by selectively validating location data based on confidence scores and data source reliability. High-confidence data from reputable sources requires minimal verification, while lower-confidence data undergoes more extensive checking. This partial action approach maintains high reliability while reducing overall processing time compared to verifying every data point equally.
Solution Approach 2:
The verification process dynamically adjusts parameters such as verification depth, data source weighting, and confidence thresholds based on entity type, industry, and data criticality. This parameter changing approach optimizes the balance between verification thoroughness and processing speed, applying more rigorous verification only when necessary for high-stakes applications.
Data Source
AI summary
Techniques for automatic extraction and verification of location data are disclosed. In some embodiments, a web crawler is configured to identify uniform resource locators (URLs), including a URL for a website associated with a target entity. The web crawler is further configured to fetch a subset of webpages from the website associated with the target entity. The web crawler may restrict the webpages that are fetched from the website based, at least in part, on patterns in the first website that are indicative of where reliable location information may be found. The web crawler further identifies a primary location of the target entity within at least one webpage in the subset of webpages, populating and/or verifying the primary location of the target entity in an entity profile. The entity profile may be consumed by client applications to execute location-aware and/or location dependent functions.