Semantic Network for Enterprise Database Search Accuracy
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Solution Overview
Problem
Conventional search engines are ineffective in searching enterprise data due to their inability to consider the semantic meaning of keywords, leading to inaccurate results when searching for data with context-dependent meanings.
Innovation Solution
The implementation of a meta-model semantic network that associates semantic labels with enterprise data, enabling meaningful searches by generating semantic objects and relations, and using a learning module with a neural-network module to build models that relate semantic concepts, thereby improving database searches and model building.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional search engines are used to search enterprise data, then the search process is simple and fast, but the search accuracy is poor because semantic meaning is not considered
Solution Approach 1:
The patent introduces a semantic network as an intermediary layer between the search query and the enterprise data. This semantic network contains semantic objects and relations that mediate the matching process, allowing the system to understand semantic meanings and contextual relationships rather than performing simple keyword matching. The semantic network acts as a mediator that translates user queries into semantic concepts and matches them against the semantic representation of enterprise data.
Solution Approach 2:
The patent adds a semantic dimension to the traditional search process. Instead of searching only based on keyword matching in one dimension, the system now operates in multiple dimensions by incorporating semantic objects, relations, and contextual information. This dimensional expansion allows the search system to consider semantic meanings, hierarchical relationships, and contextual associations, thereby improving search accuracy significantly.
2Measurement precision
If semantic information is added to enterprise data, then search accuracy improves, but the data structure becomes more complex
Solution Approach 1:
The patent segments the semantic information into distinct components: semantic objects, semantic relations, and contextual metadata. This segmentation allows the complex semantic data to be organized into manageable units that can be processed independently. The semantic network is divided into nodes (semantic objects) and edges (relations), making the complex structure more manageable and easier to implement.
Solution Approach 2:
The patent implements a nested structure where semantic information is organized in hierarchical layers. The enterprise data is wrapped within semantic objects, which are connected through semantic relations. This nesting approach allows the system to maintain the original data structure while adding semantic layers on top, thereby improving search accuracy without completely redesigning the data architecture.
3Adaptability or versatility
If a meta-model semantic network is implemented, then meaningful data relationships become accessible, but the system complexity increases
Solution Approach 1:
The patent designs the semantic network to serve multiple functions: it provides semantic indexing for search, establishes data relationships for modeling, enables contextual querying, and supports knowledge representation. This multi-functionality reduces the need for separate systems for each purpose, thereby managing overall system complexity while enhancing adaptability and versatility in accessing data relationships.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system learns from user queries and search results to refine the semantic model. This feedback loop allows the semantic network to adapt to different querying patterns and improve its accuracy over time. The feedback mechanism helps the system automatically adjust to changing data structures and user needs, reducing the manual configuration complexity.
Data Source
AI summary
An enterprise database is accessed through semantic labels to develop models that enhance the database. A database of business objects is accessed, the business objects including data tables that relate semantic labels to enterprise data. One or more rules that use the semantic labels are applied to select enterprise data corresponding to the semantic labels. The selected enterprise data are used to determine modeling parameters that relate a semantic-label input set to a semantic-label output set, the semantic-label input set and the semantic-label output set each including at least one of the semantic labels. The modeling parameters are used to generate a simulation table that predicts an operational range of at least one business object corresponding to at least one of the semantic labels. The at least one business object is augmented in the database by including the simulation table in the at least one business object.