<?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;
</style></abstract></record><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%">Prateek Jain</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Kunal Verma</style></author><author><style face="normal" font="default" size="100%">Peter Z. Yeh</style></author><author><style face="normal" font="default" size="100%">Amit Sheth</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Ethan V. Munson</style></author><author><style face="normal" font="default" size="100%">Markus Strohmaier</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Moving beyond SameAs with PLATO: Partonomy detection for Linked Data</style></title><secondary-title><style face="normal" font="default" size="100%">23rd ACM Conference on Hypertext and Social Media, HT '12</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Linked Open Data Cloud</style></keyword><keyword><style  face="normal" font="default" size="100%">Mereology</style></keyword><keyword><style  face="normal" font="default" size="100%">Part of Relation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://doi.acm.org/10.1145/2309996.2310004</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pub-location><style face="normal" font="default" size="100%">Milwaukee, WI, USA</style></pub-location><pages><style face="normal" font="default" size="100%">33–42</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The Linked Open Data (LOD) Cloud has gained significant traction over the past few years. With over 275 interlinked datasets across diverse domains such as life science, geography, politics, and more, the LOD Cloud has the potential to support a variety of applications ranging from open domain question answering to drug discovery.&lt;/p&gt;

&lt;p&gt;Despite its significant size (approx. 30 billion triples), the data is relatively sparely interlinked (approx. 400 million links). A semantically richer LOD Cloud is needed to fully realize its potential. Data in the LOD Cloud are currently interlinked mainly via the owl:sameAs property, which is inadequate for many applications. Additional properties capturing relations based on causality or partonomy are needed to enable the answering of complex questions and to support applications.&lt;/p&gt;

&lt;p&gt;In this paper, we present a solution to enrich the LOD Cloud by automatically detecting partonomic relationships, which are well-established, fundamental properties grounded in linguistics and philosophy. We empirically evaluate our solution across several domains, and show that our approach performs well on detecting partonomic properties between LOD Cloud data.&lt;/p&gt;
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