Natural Language Query Execution Path Selection via Knowledge Graph Traversal
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Solution Overview
Problem
Conventional conversation systems are cumbersome and non-intuitive for users as they only recognize a fixed number of search queries and require determining user intent using pre-specified rules or models, limiting the effectiveness of natural language queries.
Innovation Solution
The system determines an execution path for natural language queries using questions and answers within a knowledge graph, selecting paths based on user feedback, allowing for generalized learning and retrieval of correct responses without requiring intent determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional systems use pre-specified rules or models to determine user intent, then the system can process queries, but the user experience becomes cumbersome and non-intuitive
Solution Approach 1:
The system performs self-learning by automatically analyzing user feedback and selecting execution paths without requiring manual programming of intent determination rules. The system serves itself by improving its own query processing capabilities through accumulated learning from user interactions.
Solution Approach 2:
The system incorporates user feedback loops where user selections and corrections are fed back into the learning mechanism. This feedback drives the system to refine its execution path selections and improve its understanding of natural language queries over time.
2Adaptability or versatility
If systems only recognize fixed search queries, then the system structure remains simple, but the system cannot handle natural language queries effectively
Solution Approach 1:
The system transitions from static fixed query recognition to dynamic natural language processing. The execution paths and knowledge graph traversals are dynamically selected based on the specific query and user feedback, allowing the system to adapt to various natural language formulations rather than requiring predetermined query structures.
Solution Approach 2:
The system uses a universal knowledge graph structure that can handle multiple types of queries through a single unified framework. Instead of requiring separate processing logic for different query types, the system uses entity extraction and knowledge graph traversal to universally process diverse natural language queries.
3Adaptability or versatility
If systems use pre-specified rules to determine user intent, then queries can be processed, but the system lacks the ability to learn and generalize from user feedback
Solution Approach 1:
The system performs preliminary learning during idle periods or between user interactions by analyzing accumulated feedback data. This allows the system to prepare improved execution paths and patterns in advance, so that when new queries arrive, the system can quickly apply learned knowledge without requiring real-time training.
Solution Approach 2:
The learning process operates continuously in the background as the system processes user interactions. Rather than requiring dedicated training sessions that interrupt service, the system continuously learns from each user feedback signal, maintaining both operational functionality and learning capability simultaneously.
Data Source
AI summary
Systems and methods are described to address shortcomings in conventional conversation systems by determining an execution path for a natural language query using questions and answers and selecting the path in a knowledge graph based on the entities in the questions and answers and the user's feedback. In some aspects, the systems and methods described provide for determining an execution path for a natural language query presented to an interactive media guidance application. The interactive media guidance application receives, from a user, a query including an input entity and an unknown term. The interactive media guidance application retrieves, from a knowledge graph, a plurality of possible responses for the query. The interactive media guidance application determines a correct response of the plurality of possible responses based on feedback from the user. The interactive media guidance application selects an execution path for the query based on the correct response.