Search Data Processing Mining Entity Information for Knowledge Queries
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Solution Overview
Problem
Current navigation and related search methods fail to provide informative shopping guide information when search queries include knowledge requirements, leading to fewer retrieved results and less informative recommendations.
Innovation Solution
A method and apparatus for processing search data that mines entity information from historical search queries with knowledge requirements, using a scoring system to extract and rank candidate entity information from search result information, thereby improving the accuracy of recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If navigation method is used to provide shopping guide information, then users can make purchase decisions step by step, but the information becomes less informative when queries include knowledge requirements and result in fewer retrieved results
Solution Approach 1:
The patent introduces an intermediary knowledge base that stores entity information extracted from historical search queries. When a user submits a query with knowledge requirements, the system queries this knowledge base to retrieve pre-extracted entity information, acting as a mediator between the user's knowledge-based query and the commodity search results, thereby maintaining informative guidance even when direct search results are limited
Solution Approach 2:
The system performs preliminary extraction of entity information from historical search queries and stores it in the knowledge base before actual user queries occur. This preliminary action ensures that when users submit knowledge-based queries, relevant entity information is already prepared and can be immediately retrieved, avoiding the information loss that would occur if extraction happened in real-time
2Loss of information
If related search method is used to provide refined queries, then users can redirect search, but the recommendations fail to meet user needs when queries include knowledge requirements
Solution Approach 1:
The knowledge base serves as an intermediary that bridges the gap between user knowledge-based queries and commodity recommendations. Instead of relying solely on similar query patterns, the system queries the knowledge base for entity information relevant to the user's knowledge requirements, then uses this information to generate accurate recommendations, thereby meeting both knowledge requirements and recommendation accuracy
Solution Approach 2:
The system changes the parameter for recommendation generation from query similarity (used in traditional related search) to entity information relevance. By extracting and storing entity information from historical queries and using this as the basis for recommendations, the system adapts to knowledge requirements while maintaining high accuracy in meeting user needs
Data Source
AI summary
The disclosure provides a method and apparatus for processing search data. For a historical search query that includes a knowledge requirement, the disclosure mines entity information for the historical search query and uses that as an answer recommended to users. Thus, the accuracy of entity information recommended to users is improved, and the current problem of poor search results for a historical search query that includes a knowledge requirement is solved.