<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sarasi Lalithsena</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Amit Sheth</style></author><author><style face="normal" font="default" size="100%">Prateek Jain</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automatic Domain Identification for Linked Open Data</style></title><secondary-title><style face="normal" font="default" size="100%">2013 IEEE/WIC/ACM International Conferences on Web Intelligence, WI 2013</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">dataset search</style></keyword><keyword><style  face="normal" font="default" size="100%">Domain Identification</style></keyword><keyword><style  face="normal" font="default" size="100%">Linked Open Data Cloud</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1109/WI-IAT.2013.206</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Atlanta, GA, USA</style></pub-location><pages><style face="normal" font="default" size="100%">205–212</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;Linked Open Data (LOD) has emerged as one of the largest collections of interlinked structured datasets on the Web. Although the adoption of such datasets for applications is increasing, identifying relevant datasets for a specific task or topic is still challenging. As an initial step to make such identification easier, we provide an approach to automatically identify the topic domains of given datasets. Our method utilizes existing knowledge sources, more specifically Freebase, and we present an evaluation which validates the topic domains we can identify with our system. Furthermore, we evaluate the effectiveness of identified topic domains for the purpose of finding relevant datasets, thus showing that our approach improves reusability of LOD datasets.&lt;/p&gt;
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