Time-Weighted Evidence for Question Answering
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Solution Overview
Problem
Users face information overload and difficulty in finding relevant information due to the vast amount of data available, and existing QA systems lack effective mechanisms to focus on time-weighted evidence, leading to inaccuracies in answering questions.
Innovation Solution
A method and system that utilize time-weighted closures based on dimensions of evidence, where a time-based weighting function, such as a bell curve or Gaussian function, is applied to hypothesis evidence to focus on specific time frames, enhancing the accuracy of question answering by emphasizing more recent and relevant information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If all available evidence from the corpus is used to answer questions, then the quantity of information increases, but the accuracy and relevance of answers deteriorate due to information overload and inclusion of outdated data
Solution Approach 1:
The patent applies parameter changes by introducing a time-based weighting function that transforms the static evidence corpus into a dynamically weighted set. Each evidence item is assigned a weight based on its temporal distance from the current time, effectively changing the parameter of evidence relevance. This resolves the contradiction by maintaining quantity of evidence while improving answer accuracy through temporal parameter transformation.
Solution Approach 2:
The patent implements dynamics by making the evidence weighting dynamic rather than static. The weighting function continuously adapts based on the time focus parameter, allowing the system to dynamically adjust which evidence items are most relevant. This dynamic approach resolves the contradiction by enabling the system to process large quantities of evidence while automatically prioritizing timely and accurate information.
2Adaptability or versatility
If evidence from all time periods is treated equally, then comprehensive coverage is achieved, but the relevance of answers to current contexts deteriorates
Solution Approach 1:
The patent applies local quality by assigning different weights to different evidence items based on their temporal characteristics. Instead of treating all evidence uniformly, the system applies localized quality assessment through the weighting function, giving higher importance to evidence from relevant time periods. This resolves the contradiction by maintaining comprehensive coverage while enhancing relevance through differentiated local quality assignment.
Solution Approach 2:
The patent implements preliminary action by pre-weighting evidence items according to their temporal relevance before the actual question answering process. The time-based weighting function is applied in advance to the evidence corpus, preparing a relevance-ranked set of evidence that adapts to the specific time focus of each query. This preliminary weighting resolves the contradiction by ensuring both comprehensive coverage and high relevance from the outset.
3Measurement precision
If a time-based weighting function is applied to evidence, then answer relevance to current context improves, but the complexity of the question answering system increases
Solution Approach 1:
The patent applies mechanics substitution by replacing complex manual or rule-based evidence selection mechanisms with a mathematical time-based weighting function. Instead of using intricate logical rules to determine evidence relevance, the system uses a parametric function that automatically computes weights based on temporal distance. This substitution resolves the contradiction by improving answer relevance while managing system complexity through mathematical abstraction.
Solution Approach 2:
The patent uses parameter changes to manage complexity by introducing a time focus parameter that controls the weighting behavior. Rather than implementing complex algorithms, the system adjusts the time focus parameter to adapt to different query contexts, allowing flexible control over evidence relevance. This parameter-based approach resolves the contradiction by improving answer relevance while keeping the system structure relatively simple and manageable.
Data Source
AI summary
A mechanism is provided in a data processing system for question answering using time weighted evidence. The mechanism receives an input question. The mechanism determines a time focus for the input question and defines a weighting function. The weighting function is a bell curve having a peak at the time focus on a time axis. The mechanism decomposes the input question into one or more queries and applies the one or more queries to a corpus of information to obtain a set of hypothesis evidence. Each item of information within the hypothesis evidence has an associated time value. The mechanism weights the set of hypothesis evidence based on the associated time values according to the weighting function to form time weighted evidence and generates hypotheses for answering the input question based on the time weighted evidence.