Knowledge Graph Tree Ranking for Natural Language Query Ambiguity
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Solution Overview
Problem
Conventional systems fail to accurately interpret natural language queries due to ambiguity in terms, often considering multiple interpretations without context, leading to irrelevant results.
Innovation Solution
A media guidance application that isolates terms from a query, uses a knowledge graph to identify candidate components, matches and filters pairs based on association strength, and selects the most relevant meaning by considering command terms and additional query context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional systems consider multiple interpretations of ambiguous terms without context, then they cover more possible meanings, but the relevance and accuracy of results deteriorates
Solution Approach 1:
The system segments the query into individual terms and identifies ambiguous terms separately. Each ambiguous term is then analyzed in the context of other terms in the query using the knowledge graph, rather than treating the entire query as a single unit or considering all interpretations equally.
Solution Approach 2:
The knowledge graph serves as an intermediary structure that stores pre-computed relationships between terms. When analyzing ambiguous terms, the system queries the knowledge graph to find contextually relevant interpretations, rather than generating and evaluating all possible meanings from scratch.
2Measurement precision
If the system uses a knowledge graph to identify candidate components and filter by association strength, then the precision of query understanding improves, but the processing time and computational resources increase
Solution Approach 1:
The knowledge graph is pre-computed and stored with association strengths between terms and their candidate components. This preliminary organization of data allows the system to quickly query and filter interpretations during actual query processing, rather than performing complex analysis in real-time.
Solution Approach 2:
The system uses association strength as a parameter to filter candidate interpretations. By adjusting the threshold for association strength, the system can balance between precision (higher threshold) and processing speed (lower threshold), optimizing performance based on specific query requirements.
Data Source
AI summary
Methods and systems provide for accessing a knowledge graph comprising a plurality of nodes, each node representing an entity and connected to at least one other node via an edge. The methods and systems may define, based on the knowledge graph, first and second tree data structures comprising a first subset of the plurality of nodes and a second subset of the plurality of nodes, respectively. The first and second tree data structures are used to determine candidate answers to a query based on distances between nodes of the first subset and distances between nodes of the second subset. The candidate answers are ranked based at least in part on the distances between nodes of the first subset and the distances between nodes of the second subset. At least one of the candidate answers is output, as an answer to the query, based on the ranking.