Knowledge Graph Data Model Acquisition via Automatic Object Type Determination
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Solution Overview
Problem
Existing methods for constructing knowledge graphs require manual editing or reuse of object types with varying degrees of external dependencies, making it labor-intensive and inefficient to mine and summarize data models from diverse sources.
Innovation Solution
A method that automatically determines and summarizes object types from knowledge entries in a Subject-Predicate-Object (SPO) form, using a preset rule to generate a data model that matches the semantics of the knowledge entry, thereby reducing labor costs and eliminating external dependencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual editing or reuse of object types is used to construct knowledge graphs, then the construction process can be completed with existing methods, but it requires labor-intensive operations and has external dependencies
Solution Approach 1:
The system automatically determines object types by analyzing knowledge entries itself, without requiring manual editing or external tools. The automatic determination unit processes knowledge entries to identify object types, relationships, and entities, making the system self-sufficient and eliminating dependency on external resources while maintaining ease of construction
Solution Approach 2:
The patent replaces the mechanical manual editing process with an automated computational system. The automatic determination unit uses algorithmic processing to analyze knowledge entries and determine object types, substituting human labor with automated mechanical processing that operates continuously without fatigue or interruption
2Adaptability or versatility
If manual editing is used to determine object types, then external dependencies can be reduced, but it increases labor costs and reduces efficiency
Solution Approach 1:
The system performs automatic determination of object types using内置 (built-in) analysis capabilities, making it self-sufficient without requiring external tools or manual intervention. The automatic determination unit processes knowledge entries independently, achieving both independence from external dependencies and elimination of time-consuming manual operations
Solution Approach 2:
The automated system operates continuously to determine object types, maintaining uninterrupted processing of knowledge entries. This continuous automated operation eliminates the intermittent nature of manual work and achieves both independence from external dependencies and significant reduction in time consumption
3Quantity of substance
If data models are constructed from diverse sources, then the knowledge graph becomes more comprehensive, but it becomes more difficult to mine and summarize schemas
Solution Approach 1:
The patent implements a universal automatic determination unit that handles multiple functions: identifying entities, determining relationships, and classifying object types. This multi-functional system processes diverse knowledge entries from various sources through a unified approach, achieving comprehensiveness while managing complexity through functional integration
Solution Approach 2:
The system changes the parameter of object type classification by automatically determining appropriate types based on knowledge entry analysis. This parameter transformation converts unstructured diverse data into structured classified information, enabling comprehensive knowledge graph construction from multiple sources while simplifying the summarization process through automated parameter assignment
Data Source
AI summary
Embodiments of the present disclosure provide to a method and a device for acquiring a data model in a knowledge graph, an apparatus and a storage medium. The method includes: receiving a knowledge entry describing a relationship between an entity and an object; determining a plurality of candidate object types of the object according to at least one of the entity, the relationship and the object; determining an object type for generating a data model that matches the knowledge entry from the plurality of candidate object types based on a preset rule; and generating the data model based at least on the object type.