Knowledge Graph Suggestion Nodes for Changed Data Assets
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Solution Overview
Problem
Existing knowledge graphs lack efficient mechanisms to automatically suggest new nodes or relationships based on changes made to the graph, leading to incomplete or overlooked data connections.
Innovation Solution
A method and system that analyze changes to a knowledge graph, identify candidate terms using natural language processing, and generate suggestions for new nodes or edges by comparing these terms with existing graph data, allowing user interaction for acceptance or rejection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If automatic analysis of changes is implemented to generate suggestions for new nodes or relationships, then the completeness of data connections is improved, but the computational resources and processing time are increased
Solution Approach 1:
The system performs preliminary analysis by identifying candidate terms in changed data assets and comparing them with existing terms in the knowledge graph before generating suggestions. This preliminary filtering action reduces the computational burden by only processing relevant terms rather than performing exhaustive analysis of all possible connections.
Solution Approach 2:
The system applies different processing strategies to different types of terms and changes. Candidate terms that match existing terms are handled differently from those that don't match, and suggestions are generated selectively based on the specific type of change detected in the data asset.
2Measurement precision
If comprehensive term comparison is performed to identify all potential relationships, then the accuracy of suggested connections is improved, but the processing time is increased
Solution Approach 1:
The system performs preliminary identification of candidate terms and their correspondence with existing terms before conducting detailed comparison analysis. This preliminary sorting and grouping action enables efficient subsequent processing by organizing data in advance.
Solution Approach 2:
The system changes the state of terms by normalizing them (converting to lowercase, removing whitespaces, stemming) to enable accurate comparison. This parameter transformation ensures that terms are compared in a standardized form, improving accuracy while enabling efficient matching.
3Loss of information
If all candidate terms are processed to generate suggestions, then the completeness of the knowledge graph is improved, but the device complexity is increased
Solution Approach 1:
The system segments the term comparison process into distinct phases: identifying candidate terms, comparing with existing terms, grouping by correspondence, and generating suggestions. This segmentation of the processing pipeline manages complexity by breaking down the overall task into manageable, modular steps.
Solution Approach 2:
The system introduces intermediary data structures (first data set, second data set, third data set) to facilitate the comparison process. These intermediate groupings act as mediators between the raw candidate terms and the final suggestions, simplifying the overall processing logic.
Data Source
AI summary
Approaches are described for generating suggestions for new nodes or new relationships in a knowledge graph based on content of data assets represented by existing nodes in the knowledge graph. The knowledge graph is defined by nodes connected by edges. A method includes determining that a data asset represented by a root node of a knowledge graph has been changed, where the changed data asset is represented by a version node connected to the root node. The changed data asset is processed, including: identifying one or more candidate terms in the changed data asset, and comparing each candidate term with each of one or more existing terms from data assets of the knowledge graph other than the changed data asset to obtain (i) one or more of the candidate terms that do not correspond to any existing term or (ii) one or more candidate terms that each corresponds to a respective existing term that is not related to the version node representing the changed data asset. A suggestion node is generated for each of the obtained candidate terms, each suggestion node connected to the version node representing the changed data asset, wherein each suggestion node indicates a suggestion for a new node or a new edge in the knowledge graph. Information indicative of each suggestion is displayed on a user interface.