Intergrating a Minimal Differentiator Expressions Approach into CBR for Linguistic Pattern reuse in Crime Relation: Proposed Method

Autor:	Zuriahati Mohd Yunos, Habibollah Haron, M. Shukor Talib, Naomie Salim, M. Ikhwan Syafiq
Rok vydání:	2020
Předmět:	Differentiator Decision support system Relation (database) Computer science Process (engineering) Reuse Relationship extraction Classifier (UML) Linguistics Domain (software engineering)
Zdroj:	IOP Conference Series: Materials Science and Engineering. 864:012078
ISSN:	1757-899X 1757-8981
Popis:	The relation extraction of crime news can help the monitoring specialists to accelerate the crime investigation. However, constructing patterns or designing templates manually requires domain experts. Also the built patterns do not guarantee complete differentiation among different relation instances. The automatic detection of crime entities and relationship among entities can help the regulatory authorities to accelerate the crime investigation and decision support instead of being reliant on manual process. This study aims to increase the effectiveness of the extraction of crime entities and relationship among entities based on the determination of crime lingusitic pattern using Minimal Differentiator Expressions (MDEs) that represent the cases that will be used by the CBR classifier. The proposed extraction methods can help in compiling a highly accurate and machine-understandable crime knowledge bases which can support the regulatory authorities’ investigation. This paper conducted on our proposed MDEs algorithm for linguistic pattern reuse in CBR approaches.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::6423c6958b70f33d8c2a70ff7686e6ef https://doi.org/10.1088/1757-899x/864/1/012078 Zobrazit plný text záznamu