Knowledge Asset Scheduling via Query Forecasting
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Solution Overview
Problem
Existing knowledge base systems face challenges in determining the optimal timing and granularity for adding knowledge assets, leading to incomplete information, unnecessary costs, and false positive results due to insufficient or overly detailed data, with existing tools failing to adequately address these issues.
Innovation Solution
A method that constructs query and topic time series using Natural Language Processing, generates a decision tree through a Seasonal Autoregressive Integrated Moving Average (sARIMA) forecasting model, and navigates this tree to schedule the addition of future knowledge assets, ensuring timely and relevant information is added to the knowledge base.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If knowledge assets are added to the knowledge base, then the completeness of information improves, but the cost of acquiring and maintaining assets increases
Solution Approach 1:
The system performs preliminary analysis of query patterns and forecasted user needs to determine optimal timing for adding knowledge assets. By predicting future query trends using time series analysis and machine learning models, the system schedules asset additions in advance before queries occur, ensuring information completeness while avoiding premature additions that would incur unnecessary costs.
2Loss of time
If knowledge assets are added too soon, then information is available earlier, but costs are incurred earlier than necessary and false positive results occur
Solution Approach 1:
The system continuously monitors actual query patterns against forecasted patterns and uses this feedback to refine future scheduling decisions. By comparing predicted query volumes with actual usage, the system adjusts asset addition timing to optimize the balance between early information availability and cost efficiency, reducing false positives from premature additions.
Solution Approach 2:
The system dynamically adjusts scheduling parameters such as the timing and granularity of knowledge asset additions based on forecasted query patterns. By changing these parameters according to predicted user needs, the system ensures assets are added at optimal times when they will be most useful, avoiding both premature additions and delays.
3Reliability
If knowledge assets are added too soon, then information is available, but the knowledge base becomes subject to false positive results
Solution Approach 1:
The system performs preliminary validation and quality assessment of knowledge assets before scheduling their addition to the knowledge base. By pre-evaluating assets against forecasted query requirements and quality criteria, the system ensures only relevant, high-quality assets are added, reducing the risk of false positive results while maintaining information availability.
Data Source
AI summary
For a first query classification, a query time series is constructed, the query time series comprising a set of natural language queries classified into the first query classification received per unit of time. For a first asset classification, a topic time series is constructed, the topic time series comprising a set of knowledge assets classified into the first asset classification added to a set of knowledge assets per unit of time. From the query time series and the topic time series, a decision tree is generated. By navigating the decision tree, a schedule is generated, the schedule forecasting a time at which a future knowledge asset should be added to the set of knowledge assets in time to answer a future natural language query relative to the knowledge asset.