Complex problem semantic understanding method based on knowledge graph

Abstract

The invention discloses a knowledge graph-based complex problem semantic understanding method, which comprises the following steps of: firstly, carrying out entity recognition and relationship extraction on a complex problem to obtain entity characteristics and relationship characteristics among entities, and generating a semantic graph of the complex problem; using the query graph prediction model to predict the semantic graph, and generating a query graph corresponding to the semantic graph; traversing in the knowledge graph according to the query graph, obtaining a query path, and obtaining an answer to the complex question according to logic calculation of the complex question. According to the method, input questions are converted into corresponding query structures, the method has excellent interpretability and accuracy, and complex questions can be accurately answered in combination with knowledge graph search.

Description

technical field [0001] The invention relates to the field of natural language, in particular to a method for semantic understanding of complex problems based on knowledge graphs. Background technique [0002] Knowledge graphs provide effective data support for tasks such as search engines, intelligent question answering, and reasoning. Among them, the intelligent question answering based on knowledge graph is an advanced form of information retrieval, which can understand the natural language questions raised by users, use the knowledge graph to query and return accurate retrieval results. At present, some achievements have been made in the understanding of simple problems. However, due to the characteristics of multiple entities, multiple relationships, and multiple logical operations in complex problems, how to understand and answer a complex problem accurately and efficiently is a challenge for current research. . [0003] In recent years, breakthroughs have been made i...

AI Technical Summary

Problems solved by technology

At present, the research on entity recognition and relationship extraction mainly recognizes and extracts the context information of questions by encoding them, ignoring the structural information existing between entities, resulting in wrong predictions of head and tail nodes in relationship extraction.

The semantic understanding method based on the query graph cannot effectively distinguish the importance of information during the learning process, resulting in information loss and forgetting, leading to prediction errors and generating noisy query graphs, making complex issues difficult to understand

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