Associative Relevancy Knowledge Profiling Architecture
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Solution Overview
Problem
Existing information retrieval systems, such as search engines and inference engines, are limited in their ability to capture and utilize user-defined associations between data and information relevant to a specific subject matter domain, thereby restricting the user's ability to impart knowledge and perform discovery processes effectively.
Innovation Solution
The development of associative relevancy knowledge profiling architectures, systems, methods, and computer program products that allow users to define and modify associations between known reference sets, creating a profile that captures relevant knowledge and can be used for discovery processes across various data and information sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users are able to define and modify associations between known reference sets, then the ability to capture and utilize domain-specific knowledge is improved, but the system complexity increases
Solution Approach 1:
The system segments knowledge representation into discrete associations between known reference sets. Users can define individual associations between reference sets (e.g., linking a disease reference set with a treatment reference set), and these segmented associations are stored in a knowledge profile. This segmentation allows flexible knowledge customization without requiring complete system redesign, resolving the contradiction by enabling adaptability through modular knowledge units.
Solution Approach 2:
The patent introduces an intermediary knowledge profile structure that mediates between users and the underlying data sources. This profile acts as a buffer layer that stores predefined associations between reference sets, allowing users to work with abstracted knowledge representations rather than directly managing complex data relationships. The intermediary profile simplifies user interaction while capturing domain-specific knowledge, thus improving adaptability without proportionally increasing system complexity.
2Loss of information
If search engines and inference engines work with and manage data and information, then information retrieval capability is improved, but the ability to incorporate user-defined knowledge associations is limited
Solution Approach 1:
The system performs preliminary action by pre-defining associations between known reference sets before actual information retrieval occurs. Users can establish knowledge profiles that contain pre-configured relationships between reference sets (e.g., associating symptom reference sets with diagnostic reference sets). When information retrieval is performed, these pre-established associations are automatically applied, enabling the system to incorporate user-defined knowledge without adding complexity to the core retrieval engine while improving both information retrieval capability and adaptability.
Data Source
AI summary
Provided are architectures, system, methods, and computer program products that provide a user with the ability to define an association of data and/or information from known reference sets perceived by the user as relevant to a subject matter domain, thereby imparting and formalizing some of the user's knowledge about the domain. An associative relevancy knowledge profiler may also allow a user to create a profile by modifying or restricting the known reference sets and windowing the results from the association as a user might refine any other analysis algorithms. An associative relevancy knowledge profiler may also be used to define a user profile used by the user and others. A user profile may be usable in various manners depending upon, for example, rights management permissions and restrictions for a user.