LLM Relation Labelling Pipeline for New Entity Coverage
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current knowledge graph solutions struggle with dynamic network environments, failing to incorporate random entity combinations and model connections for new entities, leading to false positives and inability to process tail entities effectively.
Innovation Solution
A system utilizing large language models (LLMs) to generate recommended elements based on element type relations, incorporating optimal relation generation prompts to enhance interface generation and improve relevance in network environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current knowledge graph solutions are used to identify connections based on network transactions, then existing entity connections can be established, but the system produces false positive connections and cannot handle random entity combinations
Solution Approach 1:
The patent introduces an LLM-based intermediary system that acts as a mediator between network transactions and knowledge graph construction. The LLM processes and interprets transaction data, generating structured entity relationships that are then integrated into the knowledge graph. This intermediary layer filters and validates connections, reducing false positives while maintaining the ability to handle diverse entity combinations.
Solution Approach 2:
The system dynamically adjusts parameters such as connection confidence thresholds, entity type constraints, and relation weightings based on the specific transaction context. By changing these parameters adaptively, the system can maintain high connection accuracy for well-established entity pairs while being more open to novel combinations, thus resolving the contradiction between reliability and versatility.
2Stability of the object's composition
If current systems focus on identified entity connections, then existing network relationships can be maintained, but the system cannot model connections for new or tail entities
Solution Approach 1:
The knowledge graph system transitions from a static structure to a dynamic one where entity types and relationships can evolve. The LLM continuously processes new transactions, identifying emerging entity patterns and integrating them into the graph. This dynamic approach allows the system to maintain stability for established connections while adapting to incorporate new and tail entities as they appear in the data stream.
Solution Approach 2:
The system performs preliminary processing and validation of entity transactions before full integration into the knowledge graph. By pre-processing transaction data through the LLM, the system can prepare and validate new entity connections in advance, ensuring they meet quality standards before being committed to the graph structure. This preliminary action enables safe expansion to new entities while preserving the integrity of existing connections.
3Manufacturing precision
If automated relation labelling using LLMs is implemented, then interface generation quality and relevance are improved, but computational resources and processing time increase
Solution Approach 1:
The system applies LLM-based relation labelling selectively to transactions that require higher precision, such as novel entity pairs or ambiguous relationships, while using simpler, faster methods for routine, well-understood connections. This partial application of the computationally intensive LLM approach maintains high interface generation quality for critical cases while reducing overall processing time by avoiding unnecessary LLM calls for straightforward transactions.
Data Source
AI summary
Systems and methods for generating an interface including recommended elements selected using generated element type relation labels are disclosed. An interface generation request including at least one element type is received and a set of recommended elements is generated based on element type relations between the at least one element type and additional element types associated with a network interface. The element type relations are generated by at least one large language model and at least one optimal relation generation prompt. An interface including the set of recommended elements is generated.