Knowledge graph-based question and answer implementation method and system

Abstract

The invention discloses a knowledge graph-based question and answer implementation method. The method comprises the following steps of: when a natural language query instruction of a user is received,firstly, carrying out entity mention extraction and entity linking on a query statement of the user to obtain a candidate entity set and a candidate relationship set of a query text; scoring the relationship matching degree between the query text and the candidate relationship set by using a relationship extraction technology, selecting the relationship with the highest score as a semantic relationship corresponding to the natural language query text, filtering out invalid entities in the candidate entity set based on the semantic relationship of the query text, and finding out semantic entities of the query text; constructing a query mode graph based on the extracted semantic entities and semantic relationships, and finding all matching items matched with the query mode graph in the knowledge graph, the matching items being answers retrieved by natural language query based on the knowledge graph. The invention further discloses a question and answer implementation system based on theknowledge graph. According to the invention, the user experience is optimized.

Description

technical field [0001] The present invention relates to the technical field of knowledge graph retrieval, in particular to a method and system for realizing question and answer based on knowledge graph. Background technique [0002] Knowledge graph organizes massive information in a structured way and efficiently provides answers to users' queries. Therefore, it has attracted widespread attention in academia and industry in recent years. In the knowledge graph, the query calculation mainly adopts the method of structural matching. That is, given a query pattern graph and a knowledge graph, find all matches in the knowledge graph that match the query pattern graph. [0003] The key to query knowledge graph lies in query comprehension and query calculation. To establish a question answering system for knowledge graphs, two core issues need to be solved: (1) Understand the semantics of questions - there are thousands of relationships in knowledge graphs, and a relationship ca...

AI Technical Summary

Problems solved by technology

To establish a question answering system for knowledge graphs, two core issues need to be solved: (1) Understand the semantics of questions - there are thousands of relationships in knowledge graphs, and a relationship can have thousands of questioning methods. For different question forms, Question answering systems use different representation methods, and these question representations must satisfy the ability to normalize questions with the same semantics and distinguish questions with different intentions; (2) user queries are usually expressed in natural language, such languages ​​cannot be directly expressed by knowledge map to calculate

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