![]() Entity Resolution can be defined as the process of identifying, matching, verifying accuracy and merging metadata that correspond to the same entities from several databases. The results of the study with the application of a Winnowing algorithm to find the percentage similarity to data from Google Scholar were able to present a percentage of similarities in percent with the category of mild, moderate or severe plagiarism, while also helping early detection as prevention of plagiarism.ĪRTICLE INFO ABSTRACT Crowd sourced and Entity Resolution has recently attracted significant attentions because it can harness the wisdom of crowd to improve the quality of Entity Resolution. Web scraping with CURL (Client URLs) and simple HTML DOM parser is used to retrieve title data from Google Scholar. The Google Scholar will be used to obtain data on research titles that have been previously available as comparison titles. This study uses a winnowing algorithm to find the percentage similarity between titles. Then we need a system that can detect the similarity between the titles to be submitted and the existing titles so that it is expected to reduce the occurrence of plagiarism. In submitting the title of the research, for example, for the final assignment research, not a few students who repeatedly submitted titles were rejected and considered doing plagiarism because the title proposed had already existed before. Plagiarism is an act that violates copyright and includes actions that harm others. Plagiarism in research can occur due to accident or intentional. This article is the first paper of multiple for the LBAM project. ![]() We also demonstrate the integration of MM rights in a notice compared to other existing architectures. Using simulation studies and prototypes, we demonstrate that LBAM slightly improves accuracy in harvesting treatment from Entity Resolution and Linked Data compared to existing models using SSKN. It includes a Semantic Learning Watch and Notify engine using SSKN that allows ways to find DR or Event novelties of DR according to the evolving user interests. ![]() It uses Machine Learning Models to improve the auto cataloguing of the DRs. They are harvested through a process able to catalogue the rights, interests and novelties in a scorm notice. The first process is based on the creation of a hub of multiple sources of Micro Metadata (MM) using a Semantic Enriched MM Harvestor, a Watch & Notify Engine and a Semantic Shared Knowledge Notice (SSKN). LBAM has goals to identify evolving interests of a person and to potentially propose a personal agenda, channels and activities. In this article, we proposed a different model named: Learning & Boosting Architecture Model (LBAM). Hence, the problem of matching which contents or DR belong to a specific user interest that demands more attention. However, data integration has allowed the ability to provide to users a uniform interface for multiple heterogonous data sources, metadata and users. The wide proliferation of various wireless communication systems and devices has led to the arrival of a massive amount of Digital Resources (DR) from multi-sources, various metadata and media.
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