Federated Search Result Merging via Machine Learning
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Solution Overview
Problem
Existing federated search systems burden users with the time-consuming task of manually merging, de-duplicating, and sorting results from different information sources, especially when some sources are private and not publicly accessible, requiring expertise and placing undue burden on novice users.
Innovation Solution
A simultaneous scope search system that uses a machine learning-based merging model to combine results from both public and private search engines, incorporating user information and feedback to enhance relevance, and allows query modification and impersonation to promote specific results, thereby simplifying the search process for novice users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If federated search collects results from multiple information sources, then the scope of information retrieval is improved, but the complexity of merging and managing results increases
Solution Approach 1:
The patent introduces an intermediary system that automatically merges results from multiple search engines (including public and private sources) and presents them through a unified interface. This intermediary handles the complexity of result integration, de-duplication, and relevance ranking, allowing users to access diverse information sources without manually managing the complexity of merging results.
2Ease of operation
If manual merging and sorting of search results is required, then user control over results is improved, but the time and effort required increases
Solution Approach 1:
The system performs self-service by automatically merging, de-duplicating, and ranking search results from multiple sources without requiring user intervention. The patent implements automated processes that handle result integration and presentation, freeing users from the time-consuming tasks of manual result management while maintaining ease of operation through a simple unified search interface.
3Loss of information
If federated search includes private information sources, then the completeness of search results is improved, but the difficulty of accessing and integrating sources increases
Solution Approach 1:
The patent creates a universal search system that can access and integrate both public and private information sources through a single federated search interface. The system is designed to handle multiple source types (public search engines, private databases, intranet resources) with unified access mechanisms, allowing comprehensive information retrieval without requiring users to navigate different access protocols or integration complexities for each source type.
Data Source
AI summary
Merging search results is required, for example, where an information retrieval system issues a query to multiple sources and obtains multiple results lists. In an embodiment a search engine at an Enterprise domain sends a query to the Enterprise search engine and also to a public Internet search engine. In embodiments, results lists obtained from different sources are merged using a merging model which is learnt using a machine learning process and updates when click-through data is observed for example. In examples, user information available in the Enterprise domain is used to influence the merging process to improve the relevance of results. In some examples, the user information is used for query modification. In an embodiment a user is able to impersonate a user of a specified group in order to promote particular results.