Dynamic Facet Generation via Graph Partitioning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing search engines are limited in their ability to dynamically generate facets for search results, as they rely on predefined filters that do not adapt to emerging features or intent variations in user queries, leading to inefficiencies in result filtering and user effort.
Innovation Solution
A system that extracts concepts from search results using a knowledge base, builds a graph with weighted edges representing similarity between concepts, partitions the graph into subgraphs to identify candidate facets, ranks them, and selects higher-ranked facets for output, enabling dynamic facet generation based on user intent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If predefined filters are used for search results, then the system structure is simple and easy to implement, but the system cannot adapt to emerging features or intent variations in user queries
Solution Approach 1:
The system dynamically generates facets by constructing a graph from extracted concepts and their associations, then partitioning the graph to identify candidate facets. This dynamic approach allows the system to adapt to emerging features and query intent variations without requiring predefined filters, resolving the contradiction between adaptability and complexity.
Solution Approach 2:
The system automatically extracts concepts from search results using a knowledge base, builds the concept graph, and generates facets without human intervention. This self-service mechanism enables the system to adapt to new features autonomously, improving adaptability while keeping the operational complexity manageable through automation.
2Ease of operation
If predefined filters are used for search results, then the implementation is straightforward, but user effort in filtering results increases
Solution Approach 1:
The system dynamically generates facets based on the actual search results and query intent by constructing a graph from extracted concepts and partitioning it to identify relevant candidate facets. This dynamic facet generation adapts to user needs in real-time, reducing user effort in filtering while the automated process manages implementation complexity.
Solution Approach 2:
The system uses the knowledge base to extract concepts and their associations from search results, building a graph that reflects the actual content and user intent. This feedback loop from results to facet generation ensures that the facets are relevant to the specific query and results, reducing user effort while the automated knowledge base integration manages complexity.
3Adaptability or versatility
If dynamic facet generation using graph partitioning is implemented, then adaptability to query intents improves, but computational complexity increases
Solution Approach 1:
The system segments the concept graph into subgraphs through partitioning, where each subgraph represents a candidate facet. This segmentation approach enables the system to handle large amounts of data by breaking it down into manageable parts, improving adaptability to query intents while reducing computational complexity through divide-and-conquer.
Solution Approach 2:
The system extracts key concepts and their associations from search results using a knowledge base, building a graph that captures only the relevant information needed for facet generation. This extraction process filters out unnecessary data, improving adaptability while reducing computational complexity by focusing only on essential concepts.
Data Source
AI summary
An example system includes a processor to receive concepts extracted from a result set corresponding to a query and result associations for each extracted concept. The processor is to build a graph based on the extracted concepts, wherein the graph comprises a number of nodes representing the extracted concepts and weighted edges representing similarity between concepts extracted from shared results. The processor is to partition the graph into subgraphs with vertices corresponding to candidate facets for vertices having higher sums of weighted edges. The processor is to rank the candidate facets. The processor is to select higher ranked candidate facets to use as facets. The processor is to output facets with a result set in response to the query.