Structured Query Generation for Social Network Search
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Social networking systems face challenges in efficiently searching for and retrieving specific content within complex social graphs, as existing search methods struggle to effectively utilize social-graph attributes and relationships in generating relevant search results.
Innovation Solution
The system generates structured queries by parsing unstructured text queries, identifying social-graph elements, and using a context-free grammar model to create natural-language queries that can be modified and refined, allowing users to pivot or broaden searches based on social-graph elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing search methods are used in social networking systems, then search functionality is provided, but the ability to effectively utilize social-graph attributes and relationships in generating relevant search results is insufficient
Solution Approach 1:
The system transforms unstructured text queries into structured queries by identifying and extracting social-graph elements (nodes, edges, attributes) from the text. This parameter transformation enables the search system to utilize social-graph structure, relationships, and attributes effectively, thereby improving search result relevance while maintaining adaptability to various query types
Solution Approach 2:
The patent introduces an intermediary processing layer between the user's text query and the social-graph search. This intermediary parses the text, identifies social-graph elements, and translates them into structured search queries that can effectively leverage social-graph relationships and attributes, resolving the contradiction between search precision and adaptability
2Measurement precision
If structured queries are generated by parsing unstructured text queries and identifying social-graph elements, then search result relevance is improved, but query processing complexity increases
Solution Approach 1:
The query processing system is segmented into distinct functional modules: text parsing module, social-graph element identification module, structured query generation module, and search execution module. This segmentation allows each module to specialize in a specific task, improving overall processing accuracy while making the complex system more manageable and maintainable
3Ease of operation
If grammar models are used to generate natural-language structured queries, then user-friendly search capability is enhanced, but computational resources required for query processing increase
Solution Approach 1:
The system applies grammar models selectively based on query characteristics. For simple queries that don't require complex social-graph relationships, basic parsing is sufficient. For queries involving relationships, attributes, or complex social-graph elements, the full grammar-based structured query generation is applied. This partial application reduces unnecessary computational overhead while maintaining user-friendly search capability where needed
Data Source
AI summary
In one embodiment, a method includes receiving a query input from a client system comprising one or more n-grams, sending instructions for presenting one or more suggested modifications for the query input, each suggested modification comprising references to one or more objects associated with the online social network, receiving an indication of a selection of one of the suggested modifications, parsing the query input and the selected suggested modification using a context-free grammar model to generate an executable query command, and sending instructions to the client system for presenting one or more search results corresponding to the query command.