Knowledge Graph Question Answering Entity Matching
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Solution Overview
Problem
Conventional FAQ question answering systems face challenges in accurately matching user queries with existing question-answer pairs due to limited coverage, leading to low accuracy in providing relevant information.
Innovation Solution
A knowledge graph-based question answering method that receives a query sentence, determines the target entity by extracting semantic feature vectors and using neural networks to match against entities in the knowledge graph, and outputs results corresponding to the target entity, incorporating name dictionaries and feature comparisons to enhance matching accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a conventional FAQ question answering system uses a limited Q&A pair database, then the system structure remains simple, but the matching accuracy between user queries and existing questions becomes low
Solution Approach 1:
The patent introduces a knowledge graph as an intermediary layer between the user query and the Q&A database. The knowledge graph contains entity information and relationships that serve as a bridge to map user queries to relevant entities, which then link to appropriate Q&A pairs. This intermediary structure enables more accurate matching without requiring an exhaustive Q&A database, resolving the contradiction between matching accuracy and system complexity.
Solution Approach 2:
The patent replaces the traditional mechanical keyword-matching mechanism with a semantic understanding approach using neural networks and knowledge graphs. Instead of relying on simple string matching or predefined Q&A coverage, the system uses semantic feature extraction and entity relationship reasoning to determine relevance, significantly improving matching accuracy while maintaining manageable system complexity through structured knowledge representation.
2Adaptability or versatility
If the Q&A database covers a narrow range of topics, then the system remains manageable, but it becomes difficult to match diverse user queries with existing questions
Solution Approach 1:
The patent segments the knowledge base into structured entities and relationships within a knowledge graph, rather than storing undifferentiated Q&A pairs. This segmentation allows the system to handle diverse queries by identifying relevant entities and their relationships, enabling broad query coverage without requiring a proportionally large Q&A database. The segmented structure makes the knowledge base more efficient and adaptable.
Solution Approach 2:
The knowledge graph structure serves multiple functions: it stores entity information, defines relationships between entities, enables semantic matching, and links to Q&A content. This multi-functional universal structure allows the system to handle various types of queries (factual, relational, inferential) without requiring separate specialized databases for each query type, thereby increasing adaptability while controlling knowledge base size.
Data Source
AI summary
Provided is a knowledge graph-based question answering method, including: receiving a query sentence; determining a target entity to which an entity mention in the query sentence is mapped in a knowledge graph; and outputting a result corresponding to the target entity in the knowledge graph.