Entity Resource Clustering for Digital Information Retrieval
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Solution Overview
Problem
Current digital information retrieval systems fail to accurately and comprehensively associate resources with entities, leading to inefficiencies in identifying and ranking relevant resources based on entity queries.
Innovation Solution
The system clusters documents into groups associated with proper names, generates a result list of ranked resources, and updates profiles by calculating correspondence scores and name matching scores to provide a comprehensive and relevant resource association.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional information retrieval systems are used to associate resources with entities, then the system structure is simple, but the accuracy and comprehensiveness of resource association is poor
Solution Approach 1:
The patent segments the resource association process into multiple independent modules: clustering module (groups resources by entity), ranking module (orders resources by relevance), and profile generation module (creates entity profiles). This segmentation improves measurement precision by allowing each module to specialize in one aspect of resource association while managing system complexity through modular design.
Solution Approach 2:
The patent introduces intermediary structures including entity profiles that mediate between raw resources and query results, and ranking scores that mediate between resource relevance and final presentation. These intermediaries improve the accuracy of resource association by providing structured representations that capture entity-resource relationships more precisely.
2Measurement precision
If comprehensive resource clustering is performed to improve resource association accuracy, then the relevance of results improves, but the processing time increases
Solution Approach 1:
The patent performs preliminary clustering of resources into entity groups and pre-computation of entity profiles before queries are submitted. This preliminary action stores processed information in advance, allowing the system to quickly retrieve and rank pre-clustered resources during query processing, thereby maintaining high relevance while reducing actual query processing time.
Solution Approach 2:
The patent implements partial clustering by focusing computational resources on clustering only the most relevant resource types or entities based on query patterns and usage statistics. This partial action approach maintains high relevance for critical resource associations while avoiding the time cost of comprehensively clustering all possible resources.
3Measurement precision
If multiple ranking criteria are used to improve resource ranking accuracy, then the quality of results improves, but the complexity of ranking algorithms increases
Solution Approach 1:
The patent employs multiple ranking parameters including relevance scores, recency weights, and entity profile matching degrees to improve ranking accuracy. By systematically varying these parameters and their weights based on resource types and query contexts, the system achieves high-ranking precision while managing algorithm complexity through parameterized scoring functions rather than complex decision logic.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for associating resources with entities. One of the methods includes clustering a plurality of first documents into one or more first document groups, wherein each of the one or more first document groups is associated with a proper name of an author; receiving a query that specifies a particular proper name of a particular author; generating a result list of one or more documents that satisfy the query, the documents being listed in order of rank; ranking the one or more first document groups based on the one or more documents that satisfy the query; and providing the one or more first document groups, wherein the one or more first document groups are presented in an order based on the ranking.