Web Graph Projection for Search Relevance Inference
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Solution Overview
Problem
Current information retrieval systems, such as web search engines, face challenges in presenting relevant results due to the varying degrees of relevancy and the complexity of user queries, often returning a mix of unrelated topics, which can confuse users seeking specific information.
Innovation Solution
The use of web projections in conjunction with artificial intelligence models to infer context, content, and user behavior by constructing a web graph where nodes represent objects and edges represent links, allowing for the extraction of context-sensitive sub-graphs to improve query relevance and result quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If basic word counting and natural language processing are used to rank search results, then the system can handle large amounts of data with simple methods, but the relevancy and precision of search results deteriorate due to varying degrees of topic matching
Solution Approach 1:
The patent transitions from traditional text-based search dimensions to graph-based structural dimensions. By representing web pages as nodes and hyperlinks as edges in a graph structure, the system adds a new dimensional perspective for analyzing document relationships, enabling more precise relevancy measurement through graph properties like path length, connectivity, and community structure.
Solution Approach 2:
The patent changes the parameters used for ranking from simple word counts to complex graph-based parameters. These include node degree, betweenness centrality, clustering coefficients, and other graph theoretical metrics that capture the structural relationships between documents, thereby improving measurement precision while maintaining computational feasibility.
2Adaptability or versatility
If all search results are presented to users regardless of relevancy, then the system provides comprehensive coverage of topics, but the user experience deteriorates due to confusion from mixed unrelated topics
Solution Approach 1:
The patent segments search results into distinct clusters based on graph-based community detection algorithms. Documents that are structurally connected in the web graph are grouped together, creating coherent result sets that maintain comprehensive coverage while organizing information by topical relevance, thereby improving user experience through better structure.
Solution Approach 2:
The patent introduces graph-based relevancy scoring as an intermediary mechanism between the search query and the presented results. This intermediary layer analyzes the structural relationships in the web graph to filter and rank results, acting as a mediator that maintains comprehensive coverage while eliminating unrelated topics that would confuse users.
3Device complexity
If traditional search methods are used, then the system operates with simple processing, but the ability to infer context and user behavior deteriorates
Solution Approach 1:
The patent implements feedback loops where user interaction patterns are analyzed through the graph structure. By monitoring how users navigate through linked documents and how documents are interconnected in the web graph, the system infers user behavior and contextual information, using this feedback to improve future search results while managing processing complexity through efficient graph algorithms.
Data Source
AI summary
A system and method that facilitates and effectuates making an inference related to objects of interest within a context. A web projection component can generate sub graphs by projecting objects of interest onto a web graph representing a superset or partially overlapping set of objects to the objects of interest. An inference component can provide inferences relating to the quality of the objects of interest, adjustments to the context, and patterns recognized from graphical properties of the sub graphs.