<?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>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tom Narock</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Frank van Harmelen</style></author><author><style face="normal" font="default" size="100%">Jim 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%">Crowdsourcing Semantics for Big Data in Geoscience Applications</style></title><secondary-title><style face="normal" font="default" size="100%">Semantics for Big Data: Papers from the AAAI Symposium</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2013</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Arlington, Virginia</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>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>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Dedre Gentner</style></author><author><style face="normal" font="default" size="100%">Frank van Harmelen</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%">Kai-Uwe Kühnberger</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Cognitive Approaches for the Semantic Web (Dagstuhl Seminar 12221)</style></title><secondary-title><style face="normal" font="default" size="100%">Dagstuhl Reports</style></secondary-title></titles><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/10.4230/DagRep.2.5.93</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">2</style></volume><pages><style face="normal" font="default" size="100%">93–116</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%">Frank van Harmelen</style></author><author><style face="normal" font="default" size="100%">Andreas Herzig</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Guilin Qi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Preface - Special issue on commonsense reasoning for the semantic web</style></title><secondary-title><style face="normal" font="default" size="100%">Annals of Mathematics and Artificial Intelligence</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/s10472-010-9209-7</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">58</style></volume><pages><style face="normal" font="default" size="100%">1–2</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%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Frank van Harmelen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Reasonable Semantic Web</style></title><secondary-title><style face="normal" font="default" size="100%">Semantic Web</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Automated Reasoning</style></keyword><keyword><style  face="normal" font="default" size="100%">Formal Semantics</style></keyword><keyword><style  face="normal" font="default" size="100%">Knowledge representation</style></keyword><keyword><style  face="normal" font="default" size="100%">Linked Open Data</style></keyword><keyword><style  face="normal" font="default" size="100%">Semantic Web</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.3233/SW-2010-0010</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">39–44</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;The realization of Semantic Web reasoning is central to substantiating the Semantic Web vision. However, current mainstream research on this topic faces serious challenges, which forces us to question established lines of research and to rethink the underlying approaches. We argue that reasoning for the Semantic Web should be understood as &quot;shared inference,&quot; which is not necessarily based on deductive methods. Model-theoretic semantics (and sound and complete reasoning based on it) functions as a gold standard, but applications dealing with large-scale and noisy data usually cannot afford the required runtimes. Approximate methods, including deductive ones, but also approaches based on entirely different methods like machine learning or natureinspired computing need to be investigated, while quality assurance needs to be done in terms of precision and recall values (as in information retrieval) and not necessarily in terms of soundness and completeness of the underlying algorithms.&lt;/p&gt;
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