Entity Network Search Ranking by Proximity
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Solution Overview
Problem
Existing search systems fail to effectively rank search results within entity networks, such as employee directories, based on relevance and proximity, leading to less relevant results being prioritized over closer connections within the organization.
Innovation Solution
A computer-implemented method that receives an entity search query, identifies relevant entities within the network, and ranks them based on proximity and relevance to the query submitter, using a system that includes an entity network management component, search engine, and relevance repository, which updates and weights relevance scores based on electronic communications and network proximity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing search systems are used to search entity networks, then search functionality is provided, but search results are not effectively ranked based on relevance and proximity to the query submitter
Solution Approach 1:
The system pre-calculates and stores proximity scores between entities in the entity network before search queries are executed. These proximity relationships are computed in advance based on network topology and communication patterns, allowing the search system to quickly retrieve and rank results using pre-computed relevance data without calculating everything in real-time
Solution Approach 2:
The patent replaces traditional keyword-based search ranking with a proximity-based ranking system that uses graph theory metrics (such as shortest path distance, betweenness centrality, or eigenvector centrality) to determine entity relevance. This substitutes conventional text-matching mechanisms with network-structure-based ranking, improving precision by incorporating relational context
2Measurement precision
If search results are ranked using proximity in the entity network, then more relevant results are prioritized, but system complexity increases due to additional relevance calculations
Solution Approach 1:
The system pre-computes proximity scores and stores them in a dedicated data structure or cache, so that during actual search operations, the ranking process simply retrieves these pre-calculated values rather than performing complex graph traversals in real-time. This separates the computationally intensive preprocessing phase from the query execution phase
Solution Approach 2:
The patent creates a simplified representation or index of the entity network that captures proximity relationships in a condensed form suitable for fast querying. This copy or index structure allows the system to perform ranking operations efficiently without repeatedly accessing and analyzing the full complex network graph
3Measurement precision
If proximity-based ranking is implemented, then search accuracy improves, but processing time increases due to network traversal requirements
Solution Approach 1:
The system performs network traversals and proximity calculations in advance, storing the results for rapid retrieval during search operations. This pre-computation approach trades offline processing time for faster online query performance, ensuring that when users submit search queries, the ranking is immediate based on pre-analyzed network structures
Solution Approach 2:
The patent implements approximate or bounded proximity calculations that provide sufficiently accurate results without requiring exhaustive network traversals. For example, it may limit the depth of graph traversal or use sampling techniques to estimate proximity scores, achieving acceptable accuracy with reduced computational overhead
Data Source
AI summary
A computer-implemented method for ranking search results using an entity network includes: receiving an entity search query submitted for performance on an entity network, the entity search query including at least one term and indicating a query submitter, the entity network comprising entities associated with each other by relations; identifying, based on the received search query and using one or more processors, all entities in the entity network that are responsive to the at least one term; ranking the identified entities, based on relevance with regard to the query submitter and using one or more processors, the ranking performed using proximity in the entity network; and presenting the identified entities in response to the entity search query, the entities presented in an order corresponding to the ranking.