Analyzing Semantic Concept Patterns to Detect Academic Plagiarism


Dateien zu dieser Ressource

Prüfsumme: MD5:15dddb1097f0f132eaf2f53d8b1566b6

MEUSCHKE, Norman, Nicolas SIEBECK, Moritz SCHUBOTZ, Bela GIPP, 2017. Analyzing Semantic Concept Patterns to Detect Academic Plagiarism. 6th International Workshop on Mining Scientific Publications WSOP 2017. Toronto, Canada, 15. Dez 2017 - 15. Dez 2017. In: Proceedings of the 6th International Workshop on Mining Scientific Publications - WOSP 2017. New York, USA:ACM Press, pp. 46-53. ISBN 978-1-4503-5388-5. Available under: doi: 10.1145/3127526.3127535

@inproceedings{Meuschke2017Analy-41874, title={Analyzing Semantic Concept Patterns to Detect Academic Plagiarism}, year={2017}, doi={10.1145/3127526.3127535}, isbn={978-1-4503-5388-5}, address={New York, USA}, publisher={ACM Press}, booktitle={Proceedings of the 6th International Workshop on Mining Scientific Publications - WOSP 2017}, pages={46--53}, author={Meuschke, Norman and Siebeck, Nicolas and Schubotz, Moritz and Gipp, Bela} }

<rdf:RDF xmlns:dcterms="" xmlns:dc="" xmlns:rdf="" xmlns:bibo="" xmlns:dspace="" xmlns:foaf="" xmlns:void="" xmlns:xsd="" > <rdf:Description rdf:about=""> <dc:language>eng</dc:language> <dcterms:issued>2017</dcterms:issued> <dc:rights>terms-of-use</dc:rights> <dcterms:rights rdf:resource=""/> <dcterms:abstract xml:lang="eng">Detecting academic plagiarism is a pressing problem, e.g., for educational and research institutions, funding agencies, and academic publishers. Existing plagiarism detection systems reliably identify copied text, or near copies of text, but often fail to detect disguised forms of academic plagiarism, such as paraphrases, translations, and idea plagiarism. We present Semantic Concept Pattern Analysis - an approach that performs an integrated analysis of semantic text relatedness and structural text similarity. Using 25 officially retracted academic plagiarism cases, we demonstrate that our approach can detect plagiarism that established text matching approaches would not identify. We view our approach as a promising addition to improve the detection capabilities for strong paraphrases. We plan to further improve Semantic Concept Pattern Analysis and include the approach as part of an integrated detection process that analyzes heterogeneous similarity features to better identify the many possible forms of plagiarism in academic documents.</dcterms:abstract> <dc:date rdf:datatype="">2018-03-21T10:38:01Z</dc:date> <dspace:hasBitstream rdf:resource=""/> <dc:creator>Meuschke, Norman</dc:creator> <bibo:uri rdf:resource=""/> <dcterms:available rdf:datatype="">2018-03-21T10:38:01Z</dcterms:available> <dc:contributor>Siebeck, Nicolas</dc:contributor> <dcterms:isPartOf rdf:resource=""/> <dcterms:hasPart rdf:resource=""/> <dc:creator>Siebeck, Nicolas</dc:creator> <dc:creator>Schubotz, Moritz</dc:creator> <dc:contributor>Meuschke, Norman</dc:contributor> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dcterms:title>Analyzing Semantic Concept Patterns to Detect Academic Plagiarism</dcterms:title> <dspace:isPartOfCollection rdf:resource=""/> <dc:creator>Gipp, Bela</dc:creator> <dc:contributor>Gipp, Bela</dc:contributor> <dc:contributor>Schubotz, Moritz</dc:contributor> </rdf:Description> </rdf:RDF>

Dateiabrufe seit 21.03.2018 (Informationen über die Zugriffsstatistik)

Meuschke_2-1q1kt47jsgza32.pdf 16

Das Dokument erscheint in:

KOPS Suche


Mein Benutzerkonto