Knowledge Map Question Answering via Syntax Tree Parsing
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Solution Overview
Problem
Existing question-answer systems rely on one-to-one entity mapping, resulting in weak connections between knowledge points, leading to inaccurate answers.
Innovation Solution
A knowledge map-based question-answer method that parses natural query sentences into syntax trees and generates machine query sentences using context-free grammar rules, allowing for precise querying of knowledge maps that include attributes and relationships between entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If one-to-one entity mapping is used in question-answer systems, then the system structure is simple, but the connection between knowledge points is weak and answer accuracy is low
Solution Approach 1:
The patent merges multiple knowledge points and their relationships into a unified knowledge map structure. Instead of treating each knowledge point in isolation, the system combines entities, attributes, and relationships into an integrated graph where nodes represent knowledge points and edges represent semantic relationships, enabling comprehensive information retrieval and improving answer accuracy through contextual connections.
Solution Approach 2:
The patent transitions from a one-dimensional one-to-one mapping structure to a multi-dimensional knowledge map structure. The knowledge map introduces multiple dimensions including entities, attributes, relationships, and contextual connections, allowing the system to navigate through complex knowledge spaces and retrieve accurate answers by traversing multiple relational paths simultaneously.
2Measurement precision
If natural language queries are directly processed, then the system is easy to operate, but the parsing accuracy and query precision are insufficient
Solution Approach 1:
The patent applies preliminary action by pre-processing natural language queries through structured parsing before querying the knowledge map. The system performs syntax analysis, semantic understanding, and query transformation in advance, converting unstructured natural language into structured query expressions that can be efficiently matched against the knowledge map, thereby improving parsing accuracy while maintaining ease of use.
Solution Approach 2:
The patent introduces an intermediary parsing and transformation layer between the user's natural language input and the knowledge map query. This intermediary component includes syntax analyzers, semantic interpreters, and query generators that mediate the conversion process, translating human-readable queries into machine-processable query expressions while preserving the original intent and improving query precision.
Data Source
AI summary
Knowledge map-based question-answer method, server, and storage medium are provided. The method includes obtaining a natural query sentence inputted by a user, and identifying a globally unique identifier, with respect to the knowledge map, of an entity in the natural query sentence. The knowledge map includes attributes and attribute values of entities and relationships thereof. The method also includes according to a context-free grammar rule, parsing the natural query sentence into a syntax tree, and according to the syntax tree, obtaining a logical expression corresponding to the natural query sentence. Moreover, the method includes according to the logical expression and the globally unique identifier of the entity, generating a machine query sentence corresponding to the knowledge map. Further, the method includes according to the machine query sentence, querying a question-answer result corresponding to the machine query sentence in the knowledge map, and feeding back the question-answer result to the user.