Corpus-Specific Search Hypothesis Scoring for Ambiguous Queries
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Solution Overview
Problem
Search engines face inefficiencies when users provide queries with semantically irrelevant or ambiguous terms, leading to the need to search multiple corpora, resulting in the presentation of extraneous information to users.
Innovation Solution
The system determines search hypotheses for a query, generating specific search queries for each corpus type, submits these queries to the corresponding search services, and provides results only if the hypothesis score meets a threshold, thereby focusing on relevant information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the search engine searches multiple corpora to handle ambiguous queries, then the comprehensiveness of search results is improved, but the system resource consumption increases
Solution Approach 1:
The patent segments the search process by dividing corpora into different types (e.g., video corpus, image corpus, web page corpus, book corpus, product corpus) and creating separate search hypotheses for each type. This segmentation allows the system to efficiently determine which corpus types are relevant to the user's query and search only those, rather than searching all corpora indiscriminately, thus reducing resource consumption while maintaining comprehensiveness.
Solution Approach 2:
The patent performs preliminary actions by generating search hypotheses and determining their scores before actually executing the search. The system calculates hypothesis scores based on query characteristics and corpus relevance, then uses these scores to filter which corpora should be searched. This preliminary filtering step prevents unnecessary searches in irrelevant corpora, reducing system resource consumption while ensuring comprehensive coverage of relevant corpora.
2Loss of information
If the search engine searches all corpora for every query, then the completeness of information is improved, but the time to retrieve relevant information increases
Solution Approach 1:
The patent performs preliminary hypothesis scoring and corpus type identification before executing searches. By determining which corpus types are relevant to the query in advance and calculating hypothesis scores, the system can quickly filter out irrelevant corpora and focus only on relevant ones, significantly reducing the time to retrieve relevant information while maintaining information completeness.
Solution Approach 2:
The patent segments the search space by creating distinct search hypotheses for different corpus types. This segmentation allows parallel or selective execution of searches only in relevant corpus types rather than sequentially searching all corpora, reducing the overall time to retrieve relevant information while ensuring complete coverage of relevant information sources.
3Adaptability or versatility
If the search engine presents results from multiple corpora, then the variety of information is improved, but the relevance of results to user needs decreases
Solution Approach 1:
The patent performs preliminary hypothesis scoring to determine the relevance of each corpus type to the user's query before presenting results. By calculating hypothesis scores based on query characteristics and corpus relevance, the system can filter out results from low-relevance corpora and prioritize results from high-relevance corpora, ensuring that the variety of information presented maintains high relevance to user needs.
Solution Approach 2:
The patent applies local quality by treating different corpus types differently based on their relevance to the specific query. Instead of uniformly presenting results from all corpora, the system adjusts the quality and priority of results from each corpus type based on its relevance score, ensuring that each corpus contributes appropriately to the overall result set based on its local relevance to the user's information needs.
4Use of energy by moving object
If the search engine limits the number of corpora searched, then the system resource usage is reduced, but the coverage of relevant information decreases
Solution Approach 1:
The patent performs preliminary hypothesis scoring to identify and prioritize relevant corpus types before executing searches. By calculating scores that reflect the likelihood of each corpus type containing relevant information, the system can confidently limit searches to only the top-scoring corpus types, reducing system resource usage while maintaining comprehensive coverage of relevant information through intelligent selection rather than exhaustive searching.
Data Source
AI summary
A system determines search hypotheses for a search query, each search hypothesis defining a search type and respectively corresponding to a resource corpus of a type that matches the search type; for each search hypothesis, generate a hypothesis search query based on the search query and the search type and submits the hypothesis search query to a search service to determine a search hypothesis score, and for each search hypothesis having a search hypothesis score meeting a search hypothesis threshold, providing search results for the search operation performed for the hypothesis search query determined for the search hypothesis; and for each search hypothesis not having a search hypothesis score meeting a search hypothesis threshold, not providing search results for the search operation performed for the hypothesis search query determined for the search hypothesis.