Knowledge Graph Question and Answer System for Mechanical Intelligent Manufacturing Based on Deep Learning

Miaoyuan Shi,Sang-Bing Tsai

doi:10.1155/2021/6627114

Abstract

With the development of deep learning and its wide application in the field of natural language, the question and answer research of knowledge graph based on deep learning has gradually become the focus of attention. After that, the natural language query is converted into a structured query sentence to identify the entities and attributes in the user’s natural language query and the specified entities and attributes are used to retrieve answers to the knowledge graph. Using the advantage of deep learning in capturing sentence information, it incorporates the attention mechanism to obtain the semantic vector of the relevant attributes in the query and uses the parameter sharing mechanism to insert candidate attributes into the triple in the same model to obtain the semantic vector of typical candidates. The experiment measured that under the 100,000 RDF dataset, the single entity query of the MIQE model does not exceed 3 seconds, and the connection query does not exceed 5 seconds. Under the one-million RDF dataset, the single entity query of the MIQE model does not exceed 8 seconds, and the connection query will not be more than 10 seconds. Experimental data show that the system of knowledge-answering questions of engineering of intelligent construction based on deep learning has good horizontal scalability.

Highlights

Using search engines to find the information people need from massive amounts of data has been a research in the field of information retrieval
Designing a knowledge-based answering system for intelligent engineering based on deep learning is a very popular research direction in the field of natural language processing
An integrated cause-effect graph is proposed, which has the process knowledge extracted from the text and can be used to determine visual answers based on information retrieval techniques [4]

Summary

Introduction

Designing a knowledge-based answering system for intelligent engineering based on deep learning is a very popular research direction in the field of natural language processing. E question understanding method of answering system based on knowledge graph is divided into three levels: article, entity, and relationship. An integrated cause-effect graph is proposed, which has the process knowledge extracted from the text and can be used to determine visual answers based on information retrieval techniques [4]. 2. Deep Learning-Based Knowledge Question Answering System for Mechanical Intelligent Manufacturing. Intelligent manufacturing is based on the combination of a new generation of information physics system and advanced manufacturing technology It can implement perceptual analysis, self-decision, and self-execution of the entire process of data and information and realize a new type of manufacturing mode with the most optimized benefits. Equation (5) shows that the error δ′ of the Lth layer can be obtained by calculating the error δL+1 of the L + 1 layer, and the error of any layer can be calculated by combining equations (4) and (5)

Knowledge Graph Question and Answer System Design

Data Analysis of Knowledge Graph Question Answering System

Findings

Conclusions