Intelligent Targeted Reasoning for Enterprise Search
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Solution Overview
Problem
Existing search engines perform poorly in returning relevant results from enterprise repositories, often providing extraneous information, relying on generic historical data, and lacking domain-specific adaptability, making it challenging for users to obtain targeted information.
Innovation Solution
The system employs domain-specific models and contextual information to guide searches within selected information repositories, using a reasoning engine to infer relationships between keyword terms and domain-specific data, thereby generating targeted and relevant search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional search engines use generic keyword matching, then search coverage is broad, but search result relevance deteriorates
Solution Approach 1:
The patent introduces domain-specific models as intermediary layers between the search query and the data repository. These models act as mediators that translate generic keyword searches into domain-contextualized queries, thereby improving relevance while filtering out extraneous information through structured domain knowledge representation
Solution Approach 2:
The system dynamically changes search parameters based on the identified domain. Instead of using fixed generic search parameters, the system adapts query parameters, weighting schemes, and matching criteria according to domain-specific characteristics, thereby improving search result relevance without retrieving extraneous information
2Measurement precision
If search engines rely on generic historical data, then data coverage is extensive, but domain-specific accuracy deteriorates
Solution Approach 1:
The patent segments the search system into domain-specific components. Instead of treating all searches uniformly, the system divides the search space into distinct domains with their own models, vocabularies, and reasoning rules, thereby achieving high domain-specific accuracy while maintaining overall system versatility through modular domain modules
Solution Approach 2:
The system applies local quality by tailoring search parameters, data structures, and reasoning mechanisms to each specific domain. Each domain receives customized search handling with appropriate precision levels, rather than applying a one-size-fits-all approach, thereby achieving both accuracy and adaptability
3Productivity
If traditional search methods are used, then system complexity is low, but search performance deteriorates
Solution Approach 1:
The patent implements preliminary action by pre-building domain-specific models, ontologies, and knowledge structures before search execution. This upfront preparation work, though complex, enables highly efficient and accurate search performance during actual query processing, as the system doesn't need to perform complex reasoning in real-time
Data Source
AI summary
Some embodiments enable intelligent searching data based on structure and content of models defining structure domains of interest. Embodiments are also capable of simultaneously providing context sensitive reasoning across the domains to find other relationships within the data that are not apparent using traditional search. For a respective domain-specific model, a method or system reasons relationships within the respective domain-specific model. Each relationship is between a keyword of a search query inputted by a user and a field of the domain-specific model. The method reasons the relationships by traversing the domain-specific model using domain-specific rules selected by the keyword. The method maps a first field of a first reasoned relationship to a second field of a second reasoned relationship. The first and second fields are in respective first and second domain-specific models. The method executes the search query based on the mapping, which generates search results presented to the user.