Knowledge graph question and answer training and application service system with automatically generated template

Abstract

The invention discloses a knowledge graph question and answer training system with an automatic template generation function, and the system comprises a predicate dictionary and category dictionary construction module which is used for constructing a predicate dictionary and a category dictionary in a remote supervision mode; a backbone query generation module which is used for obtaining sub-graphs of the theme entity and the answer entity of each training question and answer pair in the knowledge graph, and using variables to replace answer nodes in the sub-graphs to form backbone queries; asemantic alignment module which is used for aligning question phrases with backbone query semantic elements by using dependency syntactic analysis and a shaping linear alignment technology; a templateubiquitous module which is used for storing the dependency syntax tree, the backbone query and the corresponding relationship into a template library as templates; and a sorting model training modulewhich is used for performing classified learning on every two matching templates by using a machine learning binary classifier according to the matching degree to obtain a question template sorting model, so that the problems of high labor cost and low problem coverage rate in the prior art are solved.

Description

technical field [0001] This application relates to the field of intelligent applications, in particular to a knowledge graph question-answer training system automatically generated from templates, and a knowledge graph question-answer application service system automatically generated from templates. Background technique [0002] The method based on question and answer template plays an important role in knowledge graph question answering. This method extracts semantic features from user natural language questions by using word segmentation, named entity recognition, predicate detection, category detection, question type classification, entity linking, etc. , using the acquired semantic features to match the question templates in the template library through similarity or sorting algorithms. After the template matching is successful, the query template (usually a SPARQL query statement) is instantiated by using the entity, category and other information in the natural langua...

AI Technical Summary

Problems solved by technology

[0006] This application provides a knowledge map question-answer training and application service system automatically generated from templates, which solves the problems of high labor costs and low problem coverage in the prior art

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