Contextual Entity Ranking in Search Systems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Search engines lack the ability to provide contextually relevant information that ranks facts within broader groups, making it difficult for users to understand the prominence of specific facts among other related entities.
Innovation Solution
A method that receives lists of entities with associated scores and context facts, ranks them, and generates data structures to provide contextual information, including natural language text that synthesizes the ranking of entities within broader groups, allowing for efficient filtering of popular data and presentation of related entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If search engines provide only basic facts in response to queries, then the information delivery is simple and fast, but users cannot understand the prominence or context of the facts among other related entities
Solution Approach 1:
The system pre-computes and stores ranking data structures for entities within their respective groups (e.g., tallest mountains, longest rivers) before queries are submitted. This preliminary action allows the system to quickly retrieve contextual ranking information without performing complex computations during query processing, thus reducing response time while providing comprehensive contextual information.
Solution Approach 2:
The patent introduces an intermediary layer (ranking data structures and contextual information modules) between the basic fact retrieval and the user. This intermediary processes and organizes factual data into contextual rankings, allowing users to understand the prominence of facts without exposing the underlying computational complexity of the ranking algorithms and data processing mechanisms.
2Loss of information
If search engines rank and contextualize all facts within broader groups, then user understanding of fact prominence improves, but computational complexity increases
Solution Approach 1:
The system applies contextual ranking selectively to specific fact types and domains where contextual information adds value (e.g., geographical features, historical events) rather than uniformly to all facts. This local quality approach ensures computational resources are concentrated on generating contextual rankings for high-value domains while maintaining simple fact delivery for other areas, optimizing the balance between contextual relevance and computational power.
Solution Approach 2:
The patent implements partial action by providing contextual rankings only for the most relevant or frequently queried entities within broader groups, rather than computing exhaustive rankings for all possible entities. The system identifies and prioritizes key entities that benefit most from contextualization, performing ranking computations partially rather than completely, thus reducing computational power requirements while maintaining user understanding for critical facts.
3Loss of information
If detailed contextual information including rankings is provided for all entities, then user understanding improves, but information processing time increases
Solution Approach 1:
The system pre-computes and caches ranking data structures for entities in their respective groups before queries are submitted. This preliminary action stores contextual ranking information in advance, allowing the system to retrieve pre-computed rankings instantly during query processing rather than performing time-consuming ranking computations in real-time, thus providing detailed fact context without increasing response time.
Data Source
AI summary
In an aspect, a method includes receiving lists of entities, each list (i) having an associated score, (ii) being associated with a respective context fact, and (iii) ranking a subset of the entities, and for each of the lists of entities, generating, for each entity on the list, a data structure that references (i) the entity, (ii) the context fact associated with the list, (iii) the rank of the entity for the context fact, and (iv) the score for the list. The method can also include receiving data identifying a particular entity, selecting a particular data structure that references the particular entity, and providing, for output, data indicating (i) the context fact associated with the particular data structure that references the particular entity, and (ii) the rank of the entity for the context fact associated with the particular data structure that references the particular entity.