Knowledge Graph Construction Using Scene Elements and Target Tags
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Solution Overview
Problem
Existing knowledge graph technologies struggle to enrich knowledge effectively, limiting their application value in intelligent information services such as intelligent search, intelligent Q&A, and personalized recommendation.
Innovation Solution
A method and apparatus that determine a scene and scene elements, identify target tags based on association relationships, and establish edges between scene nodes and target tag nodes to construct a knowledge graph, utilizing scene elements as intermediaries to mine implicit relationships and enhance knowledge content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional knowledge graph construction methods are used, then the knowledge graph structure is simple and easy to maintain, but the knowledge richness and application value are insufficient
Solution Approach 1:
The patent introduces scene elements as intermediary components to bridge entities and attributes. By determining scene elements from unstructured data and establishing associations between entities, attributes, and scene elements, the system enriches knowledge graphs without requiring complex direct entity-attribute linking mechanisms, thus resolving the contradiction between knowledge richness and construction complexity
Solution Approach 2:
The patent performs preliminary processing of unstructured data to extract scene elements before constructing the knowledge graph. This preliminary action organizes raw information into structured scene elements that can be systematically associated with entities and attributes, enabling richer knowledge representation while maintaining a simplified construction process through pre-organization of data
2Adaptability or versatility
If more scene elements and attribute tags are added to enrich knowledge, then the knowledge graph provides better application values, but the processing time and computational resources increase
Solution Approach 1:
The patent segments the knowledge enrichment process into distinct modules: scene element determination, attribute tag association, and edge establishment. This segmentation allows independent optimization of each component and enables parallel processing, reducing overall processing time while maintaining comprehensive knowledge enrichment for improved application value
Solution Approach 2:
The patent dynamically adjusts the depth and breadth of knowledge enrichment by modifying parameters such as the number of scene elements to extract and the threshold for attribute tag associations. This parameter adjustment mechanism allows the system to optimize processing time based on specific application requirements while maintaining sufficient knowledge richness for valuable applications
Data Source
AI summary
A method, apparatus, device, and storage medium for constructing a knowledge graph, relates to the field of data processing, and specifically to artificial intelligence technology is provided. The method may include: determining a scene and a scene element of the scene; determining a target tag from attribute tags based on an association relationship between an entity and the scene element, and an association relationship between the entity and each of the attribute tags; and establishing an edge between a scene node and a target tag node, to obtain a knowledge graph including scene information.