Database Entry Extraction via Logical Entity Mediation
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Solution Overview
Problem
Existing database retrieval methods fail to accurately extract relevant entries when there is not a one-to-one correspondence between the query and the key, leading to inefficiencies and ambiguity, especially when multiple keys are associated with similar queries.
Innovation Solution
A method utilizing a second database with a larger number of entities to fine-tune the selection of relevant entries from a first database by linking input keywords to logical entities and matching them to entries based on identification and linking rules, thereby increasing precision and reducing ambiguity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If wildcard search is used to retrieve entries, then the number of identified results increases, but the user has to manually select the relevant result
Solution Approach 1:
The patent introduces a second database containing logical entities as an intermediary between the user's keyword query and the first database's entries. This intermediary database enables automatic disambiguation by matching keywords to logical entities and then to relevant entries, eliminating the need for manual selection while maintaining precise result retrieval.
2Measurement precision
If exact key matching is used, then retrieval precision is high, but the system fails when query does not exactly match the key
Solution Approach 1:
The second database containing logical entities serves as a mediator that bridges exact key matching and flexible querying. The system first matches the user's flexible keyword query to logical entities in the second database, then uses these entities to precisely identify relevant entries in the first database, thereby maintaining both precision and flexibility.
Solution Approach 2:
The patent segments the retrieval process into two distinct stages: first matching keywords to logical entities in the second database, then matching entities to entries in the first database. This segmentation allows the system to handle flexible queries while maintaining precise retrieval through the intermediate entity matching step.
3Adaptability or versatility
If multiple keys are associated with similar queries, then the database covers more topics, but it becomes difficult to find the specific relevant entry
Solution Approach 1:
The second database with logical entities acts as an intermediary that resolves ambiguity when multiple keys are associated with similar queries. By matching the user's keyword to logical entities first, the system can automatically identify and select the most relevant entry even when multiple entries exist, eliminating the need for manual disambiguation.
Data Source
AI summary
The present teachings generally relate to a method for extracting one or more matched entries from a first database using a second database, including the steps of: identifying a plurality of second entities from the second database by filtering a plurality of entities of the second database according to one or more identification rules; inputting at least one keyword as a query to extract the one or more matched entries from the first database; linking the at least one keyword to one or more second entities according to one or more linking rules to define one or more linked second entities; matching the one or more linked second entities to one or more entries in the first database according to one or more matching rules to define the one or more matched entries; and extracting the one or more matched entries from the first database.