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Exploiting microdata annotations to consistently categorize product offers at web scale
Meusel, Robert
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Primpeli, Anna
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Meilicke, Christian
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Paulheim, Heiko
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Bizer, Christian
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DOI:
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https://doi.org/10.1007/978-3-319-27729-5_7
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URL:
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https://www.researchgate.net/publication/300113438...
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Weitere URL:
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http://dws.informatik.uni-mannheim.de/fileadmin/le...
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Dokumenttyp:
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Konferenzveröffentlichung
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Erscheinungsjahr:
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2015
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Buchtitel:
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E-Commerce and Web Technologies : 16th International Conference on Electronic Commerce and Web Technologies, EC-Web 2015, Valencia, Spain, September 2015, revised selected papers
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Titel einer Zeitschrift oder einer Reihe:
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Lecture Notes in Business Information Processing : LNBIP
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Band/Volume:
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239
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Seitenbereich:
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83-99
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Veranstaltungstitel:
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EC-Web 2015
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Veranstaltungsort:
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Valencia, Spain
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Veranstaltungsdatum:
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September 01-02, 2015
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Herausgeber:
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Stuckenschmidt, Heiner
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Ort der Veröffentlichung:
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Berlin [u.a.]
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Verlag:
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Springer
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ISBN:
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978-3-319-27728-8 , 978-3-319-27729-5
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ISSN:
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1865-1348 , 1865-1356
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Sprache der Veröffentlichung:
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Englisch
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Einrichtung:
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Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Information Systems V: Web-based Systems (Bizer 2012-)
Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Web Data Mining (Juniorprofessur) (Paulheim 2013-2017)
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Fachgebiet:
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004 Informatik
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Freie Schlagwörter (Englisch):
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Microdata , RDFa , Structured Web Data , Classification
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Abstract:
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Semantically annotated data, using markup languages like RDFa and Microdata, has become more and more publicly available in the Web, especially in the area of e-commerce. Thus, a large amount of structured product descriptions are freely available and can be used for various applications, such as product search or recommendation. However, little efforts have been made to analyze the categories of the available product descriptions. Although some products have an explicit category assigned, the categorization schemes vary a lot, as the products originate from thousands of different sites. This heterogeneity makes the use of supervised methods, which have been proposed by most previous works, hard to apply. Therefore, in this paper, we explain how distantly supervised approaches can be used to exploit the heterogeneous category information in order to map the products to set of target categories from an existing product catalogue. Our results show that, even though this task is by far not trivial, we can reach almost 56% accuracy for classifying products into 37 categories.
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 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
 Suche Autoren in
BASE:
Meusel, Robert
;
Primpeli, Anna
;
Meilicke, Christian
;
Paulheim, Heiko
;
Bizer, Christian
Google Scholar:
Meusel, Robert
;
Primpeli, Anna
;
Meilicke, Christian
;
Paulheim, Heiko
;
Bizer, Christian
ORCID:
Meusel, Robert, Primpeli, Anna  ORCID: https://orcid.org/0000-0002-1783-2482, Meilicke, Christian ORCID: https://orcid.org/0000-0002-0198-5396, Paulheim, Heiko ORCID: https://orcid.org/0000-0003-4386-8195 and Bizer, Christian ORCID: https://orcid.org/0000-0003-2367-0237
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