Graph-Based Search Insight Generation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Search engines lack the ability to provide insightful and interesting connections between entities in search results, failing to enhance user experience by only focusing on query-specific information.
Innovation Solution
A system that utilizes a data graph to identify and provide insightful relationships, rare connections, strong secondary connections, and unique facts by analyzing entities and their relationships, incorporating a query handling system with a processor and memory to generate search results that include matched connections, sorted by popularity, and pre-computed offline for low-latency delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If search engines only provide query-specific information from documents, then the search system remains simple and fast, but the user experience lacks insightful and interesting connections between entities
Solution Approach 1:
The system pre-computes and stores insightful connections, relationships, and facts about entities in advance in a data graph structure. When a query is received, the system quickly retrieves pre-computed information about entities mentioned in the query rather than computing relationships in real-time. This allows the system to provide enriched search results with insightful connections without adding significant complexity to the query processing path.
2Productivity
If the system provides comprehensive insightful connections and relationships between entities, then user engagement improves, but processing power requirements increase
Solution Approach 1:
The system performs computationally intensive relationship analysis, path finding, and connection identification in advance during offline processing. The results are stored in a data graph that can be quickly queried during online search operations. This shifts the processing burden from online query time to offline preparation time, maintaining low processing power requirements during actual search while still providing comprehensive insightful connections.
Solution Approach 2:
The system creates a simplified data graph copy or representation of the knowledge base that is optimized for quick querying. Instead of processing the full complexity of all entity relationships during each search, the system uses pre-processed graph structures that enable fast retrieval of insightful connections with minimal computational overhead during query handling.
3Manufacturing precision
If the system analyzes and provides rare connections and strong secondary connections between entities, then the quality of search results improves, but the time required to process queries increases
Solution Approach 1:
The system identifies and stores rare connections, strong secondary connections, and insightful relationships in advance in the data graph structure. During query processing, the system simply retrieves these pre-identified connections rather than computing them in real-time. This maintains high search result quality with minimal additional query processing time.
Solution Approach 2:
The system provides different levels of connection detail locally based on query context. For each entity mentioned in a query, the system retrieves only the relevant pre-computed connections and relationships that are locally applicable to that specific query, rather than providing all possible connections globally. This optimizes the balance between result quality and processing time by delivering precisely the right amount of information for each query.
Data Source
AI summary
Implementations provide an enhanced search result to improve the user search experience. For example, the result may include insightful information relevant to the search query that was not specifically requested but that the user may find interesting, such as relationships shared between the two entities related to the query, a relationship between the two entities that does not commonly occur with another relationship shared by the entities, or strong secondary connections for an entity related to the query. In some implementations, insightful connections may also be unique facts for a particular entity. Unique facts may represent a superlative attribute of an entity such as, for example, the tallest actor, the oldest president, the most expensive stock, etc. Such shared relationships, rare relationships, and/or unique facts may be provided as part of the search results presented to the query requestor and may provide insight to the requestor about the entity.