@article {65458,
	title = {CAS-based information retrieval in semi-structured documents: CASISS model},
	journal = {Journal of Innovation in Digital EcosystemsJournal of Innovation in Digital Ecosystems},
	volume = {3},
	year = {2016},
	month = {2016},
	pages = {155-162},
	abstract = {\&nbsp; This paper aims to address the assessment the similarity between documents or pieces of documents. For this purpose we have developed CASISS (CAlculation of SImilarity of Semi-Structured documents) method to quantify how two given texts are similar. The method can be employed in wide area of applications including content reuse detection which is a hot and challenging topic. It can be also used to increase the accuracy of the information\&nbsp;retrieval process\&nbsp;by taking into account not only the presence of query terms in the given document (Content Only search {\textemdash} CO) but also the topology (position continuity) of these terms (based on Content And Structure Search {\textemdash} CAS). Tracking the origin of the information in social media, copy right management,\&nbsp;plagiarism detection, social media mining and monitoring,\&nbsp;digital forensic\&nbsp;are among other applications require tools such as CASISS to measure, with a high accuracy, the content overlap between two documents. CASISS identify elements of semi-structured documents using elements descriptors. Each semi-structured document is pre-processed before the extraction of a set of elements descriptors, which characterize the content of the elements. \&nbsp;},
	author = {Larbi GUEZOULI and Essafi, Hassane}
}
