Query Topic-Answer Pair Scoring for Ambiguous Search
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Search engines face challenges in interpreting and responding to queries that are not in the form of questions, are vague, or ambiguous, as they struggle to determine the intended topic or provide relevant factual answers.
Innovation Solution
A computer-implemented method that identifies candidate topics from a query, generates candidate topic-answer pairs, and determines scores based on the occurrence of topics and answers in annotated resources, allowing the system to decide whether to respond with answers by comparing scores to a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the search system attempts to interpret and respond to all types of queries including vague and ambiguous ones, then the system's ability to provide relevant factual answers is improved, but the complexity of query interpretation and topic identification increases
Solution Approach 1:
The system segments the query interpretation process into distinct modules: candidate topic identification, candidate answer generation, scoring/ranking, and selection. This modular approach handles the complexity of versatile query interpretation by breaking it down into manageable, specialized components that can be processed sequentially.
Solution Approach 2:
The system introduces intermediary elements including candidate topics as intermediate representations between the raw query and final answers, and annotated resources that mediate between search results and topic identification. These intermediaries facilitate the transition from ambiguous queries to structured interpretations.
2Measurement precision
If the system generates and evaluates multiple candidate topic-answer pairs to handle ambiguous queries, then the accuracy of answer selection is improved, but the computational resources and processing time required increase
Solution Approach 1:
The system generates multiple candidate topic-answer pairs beyond what is strictly necessary, then uses scoring to rank them. By producing more candidates than needed and selecting based on scores, the system ensures high accuracy without requiring exhaustive analysis of all possible interpretations, balancing thoroughness with efficiency.
Solution Approach 2:
The system changes parameters by assigning numerical scores to candidate topic-answer pairs based on multiple factors (topic relevance, answer relevance, source quality). This quantitative transformation enables efficient comparison and selection among candidates, reducing the time required for accurate answer selection.
3Reliability
If the system uses annotated resources and scoring mechanisms to rank candidate topic-answer pairs, then the relevance and accuracy of provided answers are improved, but the complexity of the evaluation and scoring process increases
Solution Approach 1:
The scoring mechanism serves multiple functions simultaneously: it evaluates topic relevance to the query, assesses answer quality, weights search result importance, and ranks candidates for selection. This multi-functional scoring system improves reliability without requiring separate complex processes for each evaluation criterion.
Solution Approach 2:
The system uses feedback from annotated resources about topic and answer occurrences to adjust and refine scores. This feedback mechanism continuously improves the reliability of answer selection by incorporating evidence from multiple sources into the scoring process.
Data Source
AI summary
A computer-implemented method can include identifying one or more candidate topics from a query. The method can generate, for each candidate topic, a candidate topic-answer pair that includes both the candidate topic and an answer to the query for the candidate topic. The method can obtain search results based on the query, wherein one or more of the search results references an annotated resource. For each candidate topic-answer pair, the method can determine a score for the candidate topic-answer pair for use in determining a response to the query, based on (i) an occurrence of the candidate topic in the annotations of the resources referenced by one or more of the search results, and (ii) an occurrence of the answer in annotations of the resources referenced by the one or more search results, or in the resources referenced by the one or more search results.