Semantic Query Retrieval via Dependency Graphs
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Solution Overview
Problem
Existing query answering systems face difficulties in accurately retrieving results for natural language queries, particularly procedural queries, due to the complexity of linguistic structures and dependencies between terms, which existing techniques fail to address effectively, especially in cold-start situations without sufficient training data.
Innovation Solution
A computer-implemented method that generates semantic representations for natural language queries and candidate query results, using triples to represent actions, roles, and values, and computes match scores based on alignment scores, allowing for accurate retrieval of query results without relying on machine learning models or ground truth training datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing information retrieval techniques (e.g., BM25) are used to match individual terms independently, then the system is simple to implement, but it fails to understand the context and dependency relationships between terms in natural language queries
Solution Approach 1:
The patent segments the natural language query into individual terms and represents each term as a separate node in a dependency graph. This segmentation allows the system to analyze each term's relationship with others independently while maintaining the overall contextual structure, resolving the contradiction between simple implementation and accurate understanding.
Solution Approach 2:
The patent introduces a dependency graph as an intermediary structure between the query terms and the retrieval process. This dependency graph captures the semantic relationships and dependencies between terms, serving as a mediator that enables accurate contextual understanding without requiring complex machine learning models, thus resolving the contradiction between accuracy and simplicity.
2Measurement precision
If machine learning approaches are used with large training datasets to build models for natural language queries, then query understanding accuracy improves, but the system cannot handle cold-start situations where training data is unavailable
Solution Approach 1:
The patent employs a self-service approach where the system uses the query itself and the dependency relationships between its terms to perform retrieval, without requiring external training data. The dependency graph is constructed on-demand from the query structure, enabling the system to handle cold-start situations while maintaining accurate query understanding through intrinsic linguistic analysis.
3Measurement precision
If the system is configured to account for exceptional cases and grammatical variations in natural language queries, then query understanding accuracy improves, but the configuration complexity and difficulty of implementation increase significantly
Solution Approach 1:
The patent changes the parameter representation from fixed grammatical rules to dynamic dependency relationships. Instead of configuring the system to handle each grammatical variation explicitly, the dependency graph automatically adapts to different query structures by representing terms and their relationships in a unified parameter space, simplifying configuration while maintaining accuracy for exceptional cases.
Data Source
AI summary
Various embodiments describe techniques for retrieving query results for natural language procedural queries. A query answering (QA) system generates a structured semantic representation of a natural language query. The structured semantic representation includes terms in the natural language query and the relationship between the terms. The QA system retrieves a set of candidate query results for the natural language query from a repository, generates a structured semantic representation for each candidate query result, and determines a match score between the natural language query and each respective candidate query result based on the similarity between the structured semantic representations for the natural language query and each respective candidate query result. A candidate query result having the highest match score is selected as the query result for the natural language query. In some embodiments, paraphrasing rules are generated from user interaction data and are used to determine the match score.