Conversational Reasoning Model for Knowledge Graph Entity Selection
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Solution Overview
Problem
Current conversational AI systems face challenges in accurately identifying relevant entities for user queries and training robust conversational reasoning models, particularly in generating natural and human-like responses across diverse domains.
Innovation Solution
The proposed solution involves a conversational reasoning model, DialKG Walker, which uses a knowledge graph to select relevant entities by associating walk paths with input contexts and dialog states, and a zero-shot relevance learning model to refine entity ranking, leveraging a large-scale human-to-human multi-turn dialogs dataset for training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a knowledge graph is used to select relevant entities for user queries, then the accuracy of entity identification is improved, but the complexity of the system increases due to the need to manage and traverse large-scale knowledge graphs
Solution Approach 1:
The patent introduces a conversational reasoning model as an intermediary between the user query and the knowledge graph. This model performs multi-hop reasoning to navigate the knowledge graph efficiently, selecting relevant entities without requiring the entire knowledge graph to be loaded or processed at once. The reasoning model acts as a mediator that translates user queries into structured knowledge graph traversals, improving entity identification accuracy while managing system complexity through intelligent query formulation and selective navigation.
2Reliability
If a large-scale human-to-human multi-turn dialogs dataset is used for training, then the robustness of the conversational reasoning model is improved, but the training time and computational resources required increase
Solution Approach 1:
The patent applies preliminary action by pre-processing the large-scale human-to-human multi-turn dialogs dataset before training. The dataset is curated and structured in advance to contain high-quality conversational examples with annotated reasoning paths. This preliminary preparation ensures that when training occurs, the model can efficiently learn from well-organized data, improving robustness while reducing actual training time by avoiding the need to process raw, unstructured data during the training phase.
3Manufacturing precision
If the DialKG Walker model is used to generate responses, then the naturalness and human-likeness of responses is improved, but the computational complexity of generating each response increases
Solution Approach 1:
The patent segments the response generation process into distinct stages: (1) understanding the user query and identifying intent, (2) performing multi-hop reasoning over the knowledge graph to retrieve relevant information, (3) synthesizing the retrieved information into a coherent response, and (4) evaluating the response for naturalness. This segmentation allows each component to be optimized independently, improving overall response quality while managing computational complexity by breaking down the complex task of generating natural responses into manageable sub-tasks that can be processed sequentially.
Data Source
AI summary
In one embodiment, a method includes receiving a query from a user from a client system associated with the user, accessing a knowledge graph comprising a plurality of nodes and edges connecting the nodes, wherein each node corresponds to an entity and each edge corresponds to a relationship between the entities corresponding to the connected nodes, determining one or more initial entities associated with the query based on the query, selecting one or more candidate nodes by a conversational reasoning model from the knowledge graph corresponding to one or more candidate entities, respectively, wherein each candidate node is selected based on the nodes corresponding to the initial entities, dialog states associated with the query, and a context associated with the query, generating a response based on the initial entities and the candidate entities, and sending instructions for presenting the response to the client system in response to the query.