<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Frank van Harmelen</style></author><author><style face="normal" font="default" size="100%">James A. Hendler</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semantics for Big Data</style></title><secondary-title><style face="normal" font="default" size="100%">AI Magazine</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.aaai.org/ojs/index.php/aimagazine/article/view/2559</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">36</style></volume><pages><style face="normal" font="default" size="100%">3–4</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Frank van Harmelen</style></author><author><style face="normal" font="default" size="100%">James A. Hendler</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Why the Data Train Needs Semantic Rails</style></title><secondary-title><style face="normal" font="default" size="100%">AI Magazine</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.aaai.org/ojs/index.php/aimagazine/article/view/2560</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">36</style></volume><pages><style face="normal" font="default" size="100%">5–14</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">While catchphrases such as big data, smart data, data intensive science, or smart dust highlight different aspects, they share a common theme: Namely, a shift towards a data-centric perspective in which the synthesis and analysis of data at an ever-increasing spatial, temporal, and thematic resolution promises new insights, while, at the same time, reducing the need for strong domain theories as starting points. In terms of the envisioned methodologies, those catchphrases tend to emphasize the role of predictive analytics, i.e., statistical techniques including data mining and machine learning, as well as supercomputing. Interestingly, however, while this perspective takes the availability of data as a given, it does not answer the question how one would discover the required data in today’s chaotic information universe, how one would understand which datasets can be meaningfully integrated, and how to communicate the results to humans and machines alike. The Semantic Web addresses these questions. In the following, we argue why the data train needs semantic rails. We point out that making sense of data and gaining new insights works best if inductive and deductive techniques go hand-in-hand instead of competing over the prerogative of interpretation.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gully Burns</style></author><author><style face="normal" font="default" size="100%">Yolanda Gil</style></author><author><style face="normal" font="default" size="100%">Yan Liu</style></author><author><style face="normal" font="default" size="100%">Natalia Villanueva-Rosales</style></author><author><style face="normal" font="default" size="100%">Sebastian Risi</style></author><author><style face="normal" font="default" size="100%">Joel Lehman</style></author><author><style face="normal" font="default" size="100%">Jeff Clune</style></author><author><style face="normal" font="default" size="100%">Christian Lebiere</style></author><author><style face="normal" font="default" size="100%">Paul S. Rosenbloom</style></author><author><style face="normal" font="default" size="100%">Frank van Harmelen</style></author><author><style face="normal" font="default" size="100%">James A. Hendler</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Samarth Swarup</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Reports on the 2013 AAAI Fall Symposium Series</style></title><secondary-title><style face="normal" font="default" size="100%">AI Magazine</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.aaai.org/ojs/index.php/aimagazine/article/view/2538</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">35</style></volume><pages><style face="normal" font="default" size="100%">69–74</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><secondary-authors><author><style face="normal" font="default" size="100%">Frank van Harmelen</style></author><author><style face="normal" font="default" size="100%">James A. Hendler</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Semantics for Big Data: Papers from the AAAI Symposium, November 15-17, 2013, Arlington, Virginia</style></title><secondary-title><style face="normal" font="default" size="100%">AAAI Symposium on Semantics for Big Data</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><pub-location><style face="normal" font="default" size="100%">Arlington, Virginia, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></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%">Raghava Mutharaju</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Philippe Cudré-Mauroux</style></author><author><style face="normal" font="default" size="100%">Jeff Heflin</style></author><author><style face="normal" font="default" size="100%">Evren Sirin</style></author><author><style face="normal" font="default" size="100%">Tania Tudorache</style></author><author><style face="normal" font="default" size="100%">Jérôme Euzenat</style></author><author><style face="normal" font="default" size="100%">Manfred Hauswirth</style></author><author><style face="normal" font="default" size="100%">Josiane Xavier Parreira</style></author><author><style face="normal" font="default" size="100%">James A. Hendler</style></author><author><style face="normal" font="default" size="100%">Guus Schreiber</style></author><author><style face="normal" font="default" size="100%">Abraham Bernstein</style></author><author><style face="normal" font="default" size="100%">Eva Blomqvist</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Very Large Scale OWL Reasoning through Distributed Computation</style></title><secondary-title><style face="normal" font="default" size="100%">11th International Semantic Web Conference (ISWC 2012), Proceedings, Part II</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Distributed Reasoning</style></keyword><keyword><style  face="normal" font="default" size="100%">Ontology Classification</style></keyword><keyword><style  face="normal" font="default" size="100%">OWL EL</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://dx.doi.org/10.1007/978-3-642-35173-0_30</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Boston, MA, USA</style></pub-location><volume><style face="normal" font="default" size="100%">7650</style></volume><pages><style face="normal" font="default" size="100%">407–414</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;Due to recent developments in reasoning algorithms of the&amp;nbsp;various OWL profiles, the classification time for an ontology has come&amp;nbsp;down drastically. For all of the popular reasoners, in order to process&amp;nbsp;an ontology, an implicit assumption is that the ontology should fit in&amp;nbsp;primary memory. The memory requirements for a reasoner are already&amp;nbsp;quite high, and considering the ever increasing size of the data to be&amp;nbsp;processed and the goal of making reasoning Web scale, this assumption&amp;nbsp;becomes overly restrictive. In our work, we study several distributed&amp;nbsp;classification approaches for the description logic EL+ (a fragment of OWL 2 EL profile). We present the lessons learned from each approach, our current results, and plans for future work.&lt;/p&gt;
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