Personalized Dialogue Knowledge Graphs for Entity Disambiguation
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Solution Overview
Problem
Conversational agents face challenges in understanding complex natural language due to polysemy, ambiguity, and the need to automatically populate user-oriented facts from dialogues, particularly in dealing with colloquial language and noisy content.
Innovation Solution
A system that utilizes a personalized knowledge graph to enhance dialogue systems by parsing speech inputs, extracting entities, and updating user profiles with contextual information from social media and domain-specific knowledge graphs, employing neural networks for disambiguation and response generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a basic front-end solution is used for answering standard FAQs, then the system is simple and easy to deploy, but it cannot provide sophisticated customer experience enhancement or handle complex natural language understanding
Solution Approach 1:
The patent implements a nested architecture where multiple processing layers are embedded within each other: speech recognition results are parsed and fed into entity extraction, which then feeds into the personalized knowledge graph, which in turn enhances the NLU engine. This nested structure allows the system to maintain modularity while achieving sophisticated natural language understanding capabilities through layered processing.
Solution Approach 2:
The personalized knowledge graph serves as an intermediary component that bridges speech recognition and natural language understanding. It extracts entities from speech inputs, stores them with contextual relationships, and provides enhanced semantic information back to the NLU engine, thereby enabling complex language handling without requiring the entire system to be overly complex.
2Speed
If polysemous words and ambiguous entities are handled using traditional methods, then the processing is fast, but the accuracy of entity disambiguation and response generation is low
Solution Approach 1:
The system implements feedback mechanisms where the personalized knowledge graph continuously refines entity disambiguation based on accumulated user interactions and contextual patterns. The NLU engine uses feedback from the knowledge graph about entity relationships and user preferences to improve disambiguation accuracy over time, while maintaining efficient processing through cached contextual information.
Solution Approach 2:
The system performs preliminary entity extraction and contextual analysis during speech recognition, preparing disambiguation data in advance before full NLU processing. By pre-processing and storing potential entity relationships in the personalized knowledge graph, the system reduces the computational burden during actual disambiguation, achieving both speed and accuracy.
3Adaptability or versatility
If user-oriented facts are automatically populated from dialogues, then the personalization capability is enhanced, but the system becomes more complex in handling colloquial language and noisy content
Solution Approach 1:
The personalized knowledge graph implements local quality by maintaining different levels of processing detail for different users and different types of information. Rather than uniformly processing all dialogue data with complex algorithms, the system applies targeted entity extraction and relationship modeling only where needed based on user context, thereby enhancing personalization while controlling overall system complexity.
4Adaptability or versatility
If general-purpose knowledge graphs are used, then the system can handle standard queries, but it cannot provide personalized responses or handle short-lived user-specific relationships
Solution Approach 1:
The knowledge graph is segmented into multiple hierarchical layers: a general-purpose knowledge graph for standard queries, domain-specific knowledge graphs for specialized contexts, and a personalized knowledge graph for user-specific information. This segmentation allows the system to query only the relevant portion of the knowledge base for each interaction, providing personalization without requiring the entire knowledge graph to be personalized, thereby managing data volume efficiently.
Data Source
AI summary
A speech recognition system includes a microphone configured to receive a speech input from one or more users and a processor in communication with the microphone. The processor is programmed to parse a plurality of candidate speech recognition results from a speech input from a user, extract one or more entities from the plurality of candidate speech recognition results, based on natural language understanding (NLU) information, select and retrieve data indicative of information of the one or more entities from a remote server, update a personal knowledge graph associated with a user profile of the user utilizing the data indicative of information of the one or more entities, and output a response to the speech input utilizing the user profile.