Entity Graph Update via Query Spike Detection
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Solution Overview
Problem
Graph-based data stores often lack information about recent or ephemeral events involving entities, such as news about celebrities or company activities, which are of interest for search queries but not captured in the data graph.
Innovation Solution
A system that identifies recent events by analyzing query spikes and content from documents, such as news articles, to determine actions associated with entities and updates the graph-based data store, enhancing search results with these findings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a graph-based data store is used to store entity information, then the data structure is organized and relationships are clearly defined, but recent or ephemeral events involving entities are not captured
Solution Approach 1:
The system proactively monitors query patterns and detects spikes before manually updating the graph database. By continuously analyzing search query volumes and automatically triggering event detection when thresholds are exceeded, the system captures recent events promptly without waiting for manual intervention or traditional data collection methods
Solution Approach 2:
The patent introduces an intermediary processing layer between user queries and the graph database. This layer analyzes search query patterns, detects anomalies indicating recent events, and extracts information from web documents. The intermediary acts as a bridge that captures ephemeral information from the web and translates it into structured updates for the graph database, preserving recent events that would otherwise be lost
2Reliability
If the graph-based data store is updated frequently to capture recent events, then the information becomes more timely, but the system complexity and computational resources increase
Solution Approach 1:
The system dynamically adjusts monitoring parameters based on detected patterns. When a query spike is detected, the system automatically increases monitoring frequency and expands document analysis scope. During normal periods, it reduces resource consumption by lowering monitoring intensity. This adaptive parameter adjustment maintains timeliness while managing system complexity and resource usage efficiently
Solution Approach 2:
The system performs self-updating by automatically detecting query spikes and triggering its own event detection and graph database update processes. The monitoring component identifies when recent events are occurring, the analysis component processes relevant documents, and the update component modifies the graph database structure accordingly. This self-service mechanism eliminates the need for external manual updates while maintaining timely information
Data Source
AI summary
Systems and methods identify recent or ephemeral events involving entities that can be used to update a data graph or provide enhanced search results. For example, a computer-implemented method includes determining at least one search term for an entity in a data graph and determining a time period having an increase in queries for the at least one search term, the increase meeting a threshold. The method may include determining documents having dates associated with the time period, the documents being responsive to the at least one search term, and analyzing the documents to determine a subject, verb, object triple, where the search term corresponds to the subject or the object of the triple. The method may also include providing an update for the data graph based on the triple. The method may also include generating text describing the triple for inclusion in a search result for the search query.