Knowledge Graph Ontology Segmentation for Industrial Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current knowledge graph structures, such as attribute graphs and semantic graphs, face limitations in efficiently modeling complex data in industrial big data scenarios, requiring enhanced semantic representation while maintaining a normative structure.
Innovation Solution
A knowledge graph construction method and system that incorporates ontology definition data with node and edge definitions, including basic, standard, and concept types to process instance data, enhancing semantic representation and structure normalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional attribute graphs or semantic graphs are used for knowledge modeling, then the structure is simple and easy to implement, but the semantic representation capability is insufficient for complex industrial big data scenarios
Solution Approach 1:
The patent segments the attribute value types into three distinct categories: basic types (for simple data types), standard types (for fixed format data), and concept types (for multi-level hierarchical structures). This segmentation allows the knowledge graph to handle complex semantic representations by appropriately categorizing different attribute values, thereby improving adaptability without unnecessarily complicating the overall structure.
Solution Approach 2:
The patent introduces a new dimension of classification by adding concept types with multi-level hierarchical structures to the traditional basic type classification. This dimensional extension enables the knowledge graph to represent complex semantic relationships and hierarchical concepts, enhancing semantic representation capability while maintaining the fundamental attribute graph structure.
2Adaptability or versatility
If complex graph structures are adopted to improve semantic representation, then the semantic capability is enhanced, but the modeling efficiency and structure normalization are compromised
Solution Approach 1:
The patent changes the parameter of attribute value type classification by introducing standard types and concept types with specific structural characteristics. This parameter change enables automated type matching and validation during data processing, which improves modeling efficiency through standardized handling while maintaining enhanced semantic representation capabilities.
Solution Approach 2:
The patent performs preliminary classification and definition of attribute value types (basic, standard, concept) during the ontology design phase. This preliminary action establishes a standardized framework that guides subsequent data processing and knowledge modeling operations, thereby improving modeling efficiency through pre-defined structures and reducing the complexity of ad-hoc processing.
3Adaptability or versatility
If basic type classification is used for attribute values, then the data type representation is simple, but the fixed format and multi-level structure representation are insufficient
Solution Approach 1:
The patent segments attribute value representation into three distinct type categories: basic types for simple data, standard types for fixed format requirements, and concept types for multi-level hierarchical structures. This segmentation enables appropriate representation for each data characteristic without requiring a single complex structure to handle all cases, thus improving adaptability while managing ontology definition complexity through clear categorization.
Data Source
AI summary
Some embodiments of this specification disclose a knowledge graph construction method and system. The method includes: obtaining ontology definition data of a knowledge graph, where the ontology definition data includes node definition data of a plurality of nodes, the node definition data includes a node attribute value type, the node attribute value type is a basic type, a standard type, or a concept type, the basic type is used to represent a data type of an attribute value, the standard type is used to represent a fixed format of the attribute value, and the concept type is used to represent a multi-level structure of the attribute value; and processing instance data based on the ontology definition data to obtain the knowledge graph that includes a node instance of a standard type attribute value and/or a concept type attribute value.