Semantics-Based Data Mapping for Metadata Correlation
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Solution Overview
Problem
Current technologies face challenges in accurately correlating and structuring semantically connected data and metadata, especially in complex business applications, where foreign key relationships need to be redefined across different systems, and semantics are not programmatically evaluable, leading to inefficiencies in data analysis and searches.
Innovation Solution
A Semantics-based Data and Metadata Mapping (SBM) infrastructure is established, utilizing a semantic dictionary that includes synonyms, specializations, abbreviations, and grammar rules to map and correlate data and metadata, enabling efficient searches and analysis by translating terms into corresponding IDs and updating documents for precise matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If foreign key relationships are manually defined for each occurrence in relational databases, then data structure accuracy is improved, but device complexity and time consumption increase significantly
Solution Approach 1:
The system performs automatic semantic analysis and correlation of data elements without requiring manual intervention. The semantic analyzer automatically identifies relationships between data elements from different systems by analyzing their semantic meanings, contexts, and usage patterns, replacing the need for manual foreign key definition while maintaining accurate data structuring
Solution Approach 2:
The patent replaces manual mechanical processes of defining foreign key relationships with an automated semantic analysis system. The system uses natural language processing, context analysis, and semantic matching algorithms to automatically establish data relationships, substituting human effort with computational intelligence
2Measurement precision
If foreign key relationships are redefined for data from different systems, then data correlation accuracy is improved, but loss of time and productivity decrease
Solution Approach 1:
The system performs preliminary semantic analysis and correlation establishment before actual data integration operations. By pre-analyzing data elements, their contexts, and potential relationships in advance, the system prepares correlation structures that can be quickly applied during data integration, reducing overall processing time while maintaining accuracy
Solution Approach 2:
The patent replaces time-consuming manual processes of data correlation with automated semantic analysis. The system uses machine learning models and semantic algorithms to rapidly analyze and correlate data from different systems, achieving high accuracy without the time loss associated with manual intervention
3Ease of operation
If semantics are not programmatically evaluable, then ease of operation is maintained, but measurement precision and automation capability deteriorate
Solution Approach 1:
The system introduces a semantic analyzer as an intermediary layer between human-readable data and computational processing. This analyzer translates semantic meanings into programmatically evaluable forms while maintaining the original semantic integrity, allowing both ease of operation and precise automated evaluation to coexist
Solution Approach 2:
The patent replaces non-programmable semantic evaluation with automated computational analysis. The system uses natural language processing, context analysis, and semantic matching algorithms to programmatically evaluate semantics, transforming qualitative semantic concepts into quantifiable computational operations
Data Source
AI summary
The present disclosure involves computer-implemented method, medium, and system for automatically correlating semantically connected data and metadata. One example method includes identifying a document that is to be analyzed using a semantics based mapping (SBM) infrastructure. A matching process is performed for the identified document using the SBM infrastructure, where the matching process identifies a plurality of matching terms within the document, the plurality of matching terms are assigned to a plurality of semantics identifiers (IDs), and each semantics ID corresponds to one or more terms in the plurality of matching terms. Each of the plurality of matching terms is replaced with a respective term ID to generate an updated document. A request to search for a target term in the document is received. The target term is translated to a target term ID based on the SBM infrastructure. The updated document is searched for one or more matching terms.