Clustered Metasearch Using NLP Realm Sorting
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Solution Overview
Problem
Existing web search systems often return irrelevant or unresponsive results due to differing ranking criteria across search engines, making it difficult for users to find relevant information.
Innovation Solution
A system that utilizes natural language processing to identify the object of a search query, matches it with high-frequency words in applicable realms, and sorts search results from multiple engines based on realm matching, along with additional factors like recency and search engine rank.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If search results are ranked using different criteria across multiple search engines, then comprehensive coverage of search results is achieved, but result relevance to user query deteriorates
Solution Approach 1:
The patent segments search results into different realms (e.g., news, academic, commercial) and applies realm-specific ranking criteria to each segment. This allows comprehensive coverage across multiple search engines while maintaining relevance within each realm by using appropriate ranking standards for that particular domain.
Solution Approach 2:
The system dynamically changes ranking parameters based on the identified realm of each search result. Different weighting factors and sorting criteria are applied depending on the realm category, allowing the system to adapt ranking behavior to match user intent for different types of information while processing results from multiple search engines.
2Measurement precision
If natural language processing is used to identify search query objects and match with realm high frequency words, then result accuracy is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary natural language processing on the search query to identify the query object and determine the applicable realm before executing the search. This preliminary classification enables subsequent filtering and ranking operations to be more efficient, as results can be evaluated against pre-established realm criteria rather than requiring complex real-time analysis of each result.
3Reliability
If search results are sorted into ordered lists based on realm matching, then irrelevant results are reduced, but processing time increases
Solution Approach 1:
The system pre-establishes realm categories and their associated high-frequency words before processing search queries. During search execution, results are quickly classified by matching against these pre-defined realms, enabling efficient filtering of irrelevant results without requiring time-consuming complex analysis of each search result's full content.
Data Source
AI summary
A clustered metasearch system receives a search query from a user. The system uses Natural Language Processing to identify an object of the search query and descriptors of the search query. The system sorts the search into an applicable realm based on the object of the search query. The system then conducts the search across a variety of search engines and collects root domains from the search results. Root domains within the same realm as the search query are prioritized and additional factors such as the presence of descriptors in the result, the recency of the result, the search engine rank of the result, and the distance from the center of the realm are used to determine the final ranking of the results. The results are then displayed to a user.