Paper Info
Title
A Chinese legal intelligent auxiliary discretionary adviser based on GA-BP NNs.
Abstract
Purpose This paper aims to build a legal intelligent auxiliary discretionary system for predicting the penalty and damage compensation values. After extensively considering current the characteristics of the current Chinese legal system, a practical legal intelligent auxiliary discretionary system based on genetic algorithm-backpropagation (GA-BP) neural network (NN) is proposed herein. Design/methodology/approach An experiment is designed to analyze cases involving mental anguish compensation in medical disputes, and a Chinese legal intelligent auxiliary discretionary adviser system is built based on a GA-BP NN. Because BP neural networks perform well for nonlinear problems and GAs can improve their ability to find optimal values, and accelerate their convergence, a combined GA-BP algorithm is used. In addition, an ontology is used to reduce the semantic ambiguities and extract the implied semantic information. Findings We confirm that a case-based legal intelligent auxiliary discretionary adviser system based on a GA-BP NN and ontology techniques has good performance in prediction. By predicting the mental anguish compensation values, the legal intelligent auxiliary discretionary adviser system can help judges to handle cases more quickly and ordinary people to discover the suggested compensation or penalty. In contrast to BP NN or SVM, the result seems more close to the actual compensation rate. Practical implications Recently, smart court has been developed in China; the purpose of which is to build the legal advice system for improving judicial justice and reducing differences in sentencing. A practical legal advice system is an urgent requirement for the judiciary. Originality/value This paper presents a study of a case-based legal intelligent auxiliary discretionary adviser system based on a GA-BP NN and ontology techniques. The findings offer advice to optimize legal intelligent auxiliary discretionary adviser systems for mental anguish compensation in medical disputes.
Year
DOI
Venue
2018
10.1108/EL-03-2017-0056
ELECTRONIC LIBRARY
Keywords
Field
DocType
Artificial intelligence,Information studies,Models,Knowledge-based systems
Convergence (routing),Ontology,World Wide Web,Computer science,Support vector machine,Operations research,Knowledge-based systems,Originality,Civil law (legal system),Semantic information,Artificial neural network
Journal
Volume
Issue
ISSN
36.0
6.0
0264-0473
Citations 
PageRank 
References 
0
0.34
11
Authors
6
Name
Order
Citations
PageRank
Ni Zhang1262.78
Yifei Pu223624.82
Sui-Quan Yang300.68
Jin-Kang Gao400.34
Zhu Wang543835.90
Jiliu Zhou645058.21