Knowledge graph construction method for intelligent power grid customer service questions and answers

Abstract

The invention discloses a knowledge graph construction method for intelligent power grid customer service questions and answers, and the method comprises the following steps: S1, carrying out the entity and assertion recognition of a question through an LSTM model, and outputting the entity and assertion of the question; S2, performing semantic enhancement processing on the entity and the assertion to obtain a matched enhanced text, and constructing a local knowledge graph; S3, based on the matched enhanced text, performing answer query to obtain a final answer of the question; and S4, constructing an integer linear programming model according to the satisfaction of the user to the final answer and the maintenance cost of the knowledge graph, and updating the local knowledge graph. According to the method, high entity and assertion recognition accuracy can be achieved, power grid domain knowledge is automatically mapped into the local knowledge graph, online updating of the service knowledge base is rapidly achieved, and high-accuracy answers are achieved with low response delay.

Description

technical field [0001] The present invention relates to a method for constructing a knowledge graph, in particular to a method for constructing a knowledge graph for smart grid customer service questions and answers. Background technique [0002] With the continuous maturity and improvement of cloud computing infrastructure and artificial intelligence technology, the intelligent question answering system based on knowledge graph (KG) is gradually applied in production environments such as power services. The question answering system (QA) extracts semantic knowledge such as entities and assertions from various data in the production system, constructs a domain knowledge base, converts the natural language of user questions into entities and assertions described in the knowledge base, and then optimizes the query algorithm of the head entity , providing intelligent question-and-answer services. The intelligent question answering system is oriented to corporate and individual...

AI Technical Summary

Problems solved by technology

[0003] However, in the smart grid customer service application scenario, due to factors such as strong closure, frequent updates of software and hardware and corresponding services, and large differences in technology stacks, there are major limitations in the construction, maintenance, and update of knowledge graphs.

On the one hand, the current research on knowledge graphs can meet the needs of automatic question answering in a limited sample space through technical means such as lexical and grammatical segmentation, associated sequence mining, and question template classification for fixed training samples, but cannot cope with the continuous development of power knowledge graphs. Newer scenarios; on the other hand, the existing question answering system will introduce other text information such as web search and documents, question retrieval outside the knowledge graph to form an open knowledge base, thereby expanding the retrieval space of the entities and assertion sets. Existence retrieval High errors, inaccurate results, etc.

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