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    Muñoz, Emir (5)
    Aldarra, Suad (1)Costabello, Luca (1)Dogdu, Erdogan (1)Hogan, Aidan (1)... View MoreSubjectData analytics (5)Linked data (3)#Know@LOD2016 (1)Classification (1)Content pattern (1)... View MoreDate Issued2016 (1)2015 (1)2014 (2)2013 (1)TypeWorkshop paper (5)

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    µRaptor: A DOM-based system with appetite for hCard elements 

    Muñoz, Emir; Costabello, Luca; Vandenbussche, Pierre-Yves (CEUR-WS.org, 2014)
    This paper describes µRaptor, a DOM-based method to extract hCard microformats from HTML pages stripped of microformat markup. µRaptor extracts DOM sub-trees, converts them into rules, and uses them to extract hCard ...
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    A linked data-based decision tree classifier to review movies 

    Aldarra, Suad; Muñoz, Emir (CEUR-WS.org, 2015)
    In this paper, we describe our contribution to the 2015 Linked Data Mining Challenge. The proposed task is concerned with the prediction of review of movies as good or bad , as does Metacritic website based on critics ...
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    A hybrid method for rating prediction using linked data features and text reviews 

    Yumusak, Semih; Muñoz, Emir; Minervini, Pasquale; Dogdu, Erdogan; Kodaz, Halife (CEUR-WS.org, 2016)
    This paper describes our entry for the Linked Data Mining Challenge 2016, which poses the problem of classifying music albums as good or bad by mining Linked Data. The original labels are assigned according to aggregated ...
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    Triplifying Wikipedia's tables 

    Muñoz, Emir; Hogan, Aidan; Mileo, Alessandra (CEUR-WS.org, 2013)
    We are currently investigating methods to triplify the content of Wikipedia's tables. We propose that existing knowledge-bases can be leveraged to semi-automatically extract high-quality facts (in the form of RDF triples) ...
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    Learning content patterns from linked data 

    Muñoz, Emir (CEUR-WS.org, 2014)
    Linked Data (LD) datasets (e.g., DBpedia, Freebase) are used in many knowledge extraction tasks due to the high variety of domains they cover. Unfortunately, many of these datasets do not provide a description for their ...
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