Social Graph Query Structuring for Search Relevance
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Solution Overview
Problem
Current social networking systems face challenges in efficiently processing unstructured text queries to retrieve relevant information from complex social graphs, leading to suboptimal search results and user experience.
Innovation Solution
A method and system that access a social graph with nodes and edges, convert unstructured text queries into structured queries referencing nodes and edges, and rank results based on degree of separation, search history, social relevance, and textual relevance, to provide accurate and relevant search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If unstructured text queries are processed directly on complex social graphs, then the system can handle diverse user inputs, but search accuracy and relevance deteriorate due to the complexity of navigating nodes and edges
Solution Approach 1:
The patent segments the unstructured text query into structured components by identifying entities, relationships, and attributes. The query is decomposed into query nodes and query edges that map to specific social graph elements, transforming an undifferentiated text string into structured search components that can be systematically processed across the social graph.
Solution Approach 2:
The patent introduces an intermediary structured query representation layer between the unstructured user input and the social graph database. This intermediate structure acts as a mediator that translates natural language queries into graph-specific search operations, bridging the gap between diverse user inputs and the complex node-edge structure of the social graph.
2Quantity of substance
If the system returns all nodes connected through multiple degrees of separation, then comprehensiveness of results is improved, but information overload and user experience deteriorate
Solution Approach 1:
The patent applies local quality by differentiating the presentation of search results based on their distance from the user node in the social graph. Nodes within a threshold degree of separation are highlighted or prioritized in the results, allowing users to quickly identify socially relevant connections while still providing access to more distant connections if needed. This creates varying levels of visibility and importance across different regions of the search results.
Solution Approach 2:
The patent implements partial action by returning only a subset of all possible connected nodes - specifically those within a threshold degree of separation. Rather than overwhelming users with every possible connection in the graph, the system selectively retrieves and presents results that are most likely to be relevant to the user's social context, providing a manageable and useful subset of the complete result set.
3Measurement precision
If the system processes queries through multiple transformation steps, then search relevance is improved, but processing time and system complexity increase
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing structural information about the social graph, such as degree of separation metrics and connection paths. When a query arrives, the system can quickly retrieve this pre-prepared structural data and combine it with the query-specific entity identification, avoiding the need to compute graph distances and relationships from scratch for each query, thus reducing processing time while maintaining relevance.
Data Source
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AI summary
In particular embodiments, a method includes receiving an unstructured text query, identifying nodes and edges from a social graph that correspond to n-grams in the text query, and then generating structured queries that include references to the identified nodes and edges. In further particular embodiments, a system comprises: one or more processors; and a memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to: receive a substantially unstructured text query, identify nodes and edges from a social graph that correspond to n-grams in the text query, and generate structured queries that include references to the identified second nodes edges.