Search Engine Relevance Ranking via Document Organizational Structure
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Solution Overview
Problem
Current search engines face challenges in accurately ranking document relevance due to inconsistencies in human assessment and automated analysis methods, which often overlook the content provider's intended ranking of documents, particularly in large datasets like the World Wide Web.
Innovation Solution
A search engine system that utilizes a bot to analyze the organizational structure of documents, such as inclusion in top ten lists and A-Z lists, to assign higher relevance scores to documents implied to be significant by their inclusion in these structures, rather than solely relying on link popularity or human analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If software bots are used to automatically analyze and rank documents based on link popularity, then efficiency and speed of document analysis are improved, but accuracy of document relevance ranking deteriorates because bots cannot recognize content provider's intended ranking
Solution Approach 1:
The patent segments the document analysis process into multiple components: traditional link popularity analysis and organizational structure analysis. The bot evaluates both aspects separately and combines them, allowing the system to maintain the efficiency of automated link analysis while adding the precision of organizational structure recognition to improve overall ranking accuracy.
Solution Approach 2:
The patent introduces organizational structure indicators (such as A-Z lists and top ten lists) as an intermediary element that bridges the gap between automated bot analysis and content provider intent. These structures serve as explicit signals that help the bot understand the publisher's ranking preferences without requiring human intervention in the analysis process.
2Measurement precision
If human assessors manually analyze and rank documents, then accuracy of relevance assessment is improved, but cost and time consumption increase significantly
Solution Approach 1:
The patent enables content providers to self-serve by organizing their documents in structured formats (A-Z lists, top ten lists). This self-organization automatically provides relevance signals to search engines, eliminating the need for expensive and time-consuming manual assessment while maintaining high accuracy through the providers' own curation efforts.
Solution Approach 2:
Organizational structures act as an intermediary that translates content provider intent into machine-understandable signals. This intermediary allows automated systems to achieve human-level assessment accuracy without the associated costs and time requirements.
3Device complexity
If traditional link popularity methods are used for document ranking, then simplicity of implementation is maintained, but relevance accuracy deteriorates due to inconsistency with content provider's intended ranking
Solution Approach 1:
The patent merges traditional link popularity analysis with organizational structure analysis into a unified ranking system. By combining these two approaches, the system maintains the simplicity and proven effectiveness of link analysis while incorporating organizational structure signals to improve relevance accuracy and align with content provider intent.
Solution Approach 2:
The patent modifies the ranking parameters by adding organizational structure indicators to the traditional link popularity metrics. This parameter change allows the system to maintain its simple implementation foundation while improving accuracy through additional relevant signals that reflect content provider preferences.
Data Source
AI summary
A search engine that provides search results which are ordered, in part, based on an automated analysis of the organizational structure of a group of documents, as indicated by the address of the documents, is disclosed. In one embodiment, a bot crawls various websites analyzing documents, and their organizational structure, in an effort to identify documents that have an implicit high quality based on their inclusion in an index. Accordingly, the search engine provides a greater weight in ranking document relevance to those documents that have been included in certain indexes, such as top ten lists, and A-Z lists.