Knowledge Graph Integration for Neural IR Model Controllability
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Solution Overview
Problem
Neural information retrieval (IR) models face performance degradation and lack controllability and explainability when operating outside their training domain due to domain drift, high costs, and susceptibility to data poisoning during continuous retraining.
Innovation Solution
A curated knowledge graph (KG) is integrated with a neural IR model, leveraging human feedback and adaptive processes to re-rank results, aligning with cognitive behavior and reducing the need for frequent model retraining by structuring entities and atoms with edges describing relationships, and allowing expert input to maintain and update the graph.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If neural IR models are continuously retrained to adapt to changing domains, then performance is maintained, but costs increase and susceptibility to data poisoning increases
Solution Approach 1:
The patent introduces a knowledge graph as an intermediary component between the neural IR model and the training data. The knowledge graph stores structured domain knowledge and relationships, allowing the system to adapt to domain drift without requiring continuous full-model retraining. This mediator enables performance maintenance while reducing the frequency and cost of retraining operations.
Solution Approach 2:
The system performs preliminary actions by pre-processing and structuring training data into a knowledge graph before it is needed for ranking. The knowledge graph is built in advance with entities, relationships, and domain knowledge that can be leveraged during inference. This preliminary structuring eliminates the need for continuous retraining while maintaining adaptability to domain changes.
2Reliability
If neural IR models are continuously retrained to adapt to changing domains, then performance is maintained, but susceptibility to data poisoning increases
Solution Approach 1:
The knowledge graph acts as a protective intermediary that filters and structures information before it reaches the neural model. By storing verified domain knowledge and relationships in the knowledge graph, the system reduces exposure to poisoned training data while maintaining the ability to adapt to legitimate domain drift through controlled knowledge graph updates rather than full model retraining.
3Ease of operation
If rule-based traditional NLP methods are used, then controllability and explainability are maintained, but performance decreases compared to neural approaches
Solution Approach 1:
The patent merges rule-based traditional NLP methods with neural network approaches by integrating a knowledge graph (which provides structured, controllable rules) with a neural IR model. The knowledge graph component maintains controllability and explainability through explicit relationships and entities, while the neural model provides high-performance ranking. This hybrid approach achieves both controllability and high performance simultaneously.
4Adaptability or versatility
If neural IR models operate outside their training domain, then versatility increases, but performance degradation occurs due to domain drift
Solution Approach 1:
The knowledge graph serves as a domain-adaptation intermediary that bridges the gap between the neural model's training domain and new application domains. When operating outside the training domain, the system queries the knowledge graph for relevant structured knowledge and relationships, which guides the neural model's predictions and prevents performance degradation from domain drift.
Solution Approach 2:
The system dynamically adapts to new domains by querying and leveraging relevant portions of the knowledge graph based on the current query and context. Rather than requiring static pre-training for each domain, the system dynamically retrieves and applies domain-relevant knowledge from the graph, enabling flexible adaptation to diverse domains while maintaining performance.
Data Source
AI summary
This invention allows the semi-automated creation and curation of a knowledge graph based on a query-atom IR ranking engine. This invention cooperates with a domain expert to smoothly and semi-automatically incorporate or restructure textual data in the knowledge graph when a suitable high-confidence response to a query cannot be found. The invention extends the conventional information retrieval approach to consumer interaction by building a structured knowledge graph. Using graph exploration the invention augments the ranking made by the underlying neural model in order to stay in sync with the constantly-changing domain of application. The ultimate goal of this invention is to allow the consistent, cognitive, consumer-driven incorporation and restructuring of relevant unstructured data in a knowledge graph which is generated by mimicking users behaviours.