Open AccessFalse Intel Detection In Crowd Source Knowledge Base
Publication year - 2021
Publication title -
international journal of advanced trends in computer science and engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3091
DOI - 10.30534/ijatcse/2021/921032021
Subject(s) - exploit , computer science , knowledge base , entity linking , encyclopedia , task (project management) , context (archaeology) , the internet , information retrieval , world wide web , scale (ratio) , data science , artificial intelligence , computer security , paleontology , physics , management , quantum mechanics , library science , economics , biology
Wikidata is widely considered as the biggest Encyclopaedia on the internet and it is the new large-scale knowledge base of the WikimediaFoundation. Its knowledge is increasingly used within Wikipedia itself and various other kinds of information systems imposing high demands on its integrity. Wikidata, it can be edited by anyone and as a result, unfortunately it frequently gets vandalized exposing all information systems using it to the risk of spreading vandalized and falsified information. In this paper a new machine learning based approach to detect vandalism in wikidata is presented. We propose sector 47 features that exploit both content and context information and we report on 4 classifiers as of increasing effectiveness tailored to this learning task.