@article {54,
	title = {Semantics for Big Data},
	journal = {AI Magazine},
	volume = {36},
	year = {2015},
	pages = {3{\textendash}4},
	url = {http://www.aaai.org/ojs/index.php/aimagazine/article/view/2559},
	author = {Frank van Harmelen and James A. Hendler and Pascal Hitzler and Krzysztof Janowicz}
}
@article {55,
	title = {Why the Data Train Needs Semantic Rails},
	journal = {AI Magazine},
	volume = {36},
	year = {2015},
	pages = {5{\textendash}14},
	abstract = {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{\textquoteright}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.},
	url = {http://www.aaai.org/ojs/index.php/aimagazine/article/view/2560},
	author = {Krzysztof Janowicz and Frank van Harmelen and James A. Hendler and Pascal Hitzler}
}
@article {57,
	title = {Reports on the 2013 AAAI Fall Symposium Series},
	journal = {AI Magazine},
	volume = {35},
	year = {2014},
	pages = {69{\textendash}74},
	url = {http://www.aaai.org/ojs/index.php/aimagazine/article/view/2538},
	author = {Gully Burns and Yolanda Gil and Yan Liu and Natalia Villanueva-Rosales and Sebastian Risi and Joel Lehman and Jeff Clune and Christian Lebiere and Paul S. Rosenbloom and Frank van Harmelen and James A. Hendler and Pascal Hitzler and Krzysztof Janowicz and Samarth Swarup}
}
@conference {400,
	title = {Crowdsourcing Semantics for Big Data in Geoscience Applications},
	booktitle = {Semantics for Big Data: Papers from the AAAI Symposium},
	year = {2013},
	month = {11/2013},
	address = {Arlington, Virginia},
	author = {Tom Narock and Pascal Hitzler},
	editor = {Frank van Harmelen and Jim Hendler and Pascal Hitzler and Krzysztof Janowicz}
}
@proceedings {310,
	title = {Semantics for Big Data: Papers from the AAAI Symposium, November 15-17, 2013, Arlington, Virginia},
	journal = {AAAI Symposium on Semantics for Big Data},
	year = {2013},
	address = {Arlington, Virginia, USA},
	editor = {Frank van Harmelen and James A. Hendler and Pascal Hitzler and Krzysztof Janowicz}
}
@article {68,
	title = {Cognitive Approaches for the Semantic Web (Dagstuhl Seminar 12221)},
	journal = {Dagstuhl Reports},
	volume = {2},
	year = {2012},
	pages = {93{\textendash}116},
	doi = {10.4230/DagRep.2.5.93},
	url = {http://dx.doi.org/10.4230/DagRep.2.5.93},
	author = {Dedre Gentner and Frank van Harmelen and Pascal Hitzler and Krzysztof Janowicz and Kai-Uwe K{\"u}hnberger}
}
@article {85,
	title = {Preface - Special issue on commonsense reasoning for the semantic web},
	journal = {Annals of Mathematics and Artificial Intelligence},
	volume = {58},
	year = {2010},
	pages = {1{\textendash}2},
	doi = {10.1007/s10472-010-9209-7},
	url = {http://dx.doi.org/10.1007/s10472-010-9209-7},
	author = {Frank van Harmelen and Andreas Herzig and Pascal Hitzler and Guilin Qi}
}
@article {78,
	title = {A Reasonable Semantic Web},
	journal = {Semantic Web},
	volume = {1},
	year = {2010},
	pages = {39{\textendash}44},
	abstract = {<p class="rtejustify">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 "shared inference," 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.</p>
},
	keywords = {Automated Reasoning, Formal Semantics, Knowledge representation, Linked Open Data, Semantic Web},
	doi = {10.3233/SW-2010-0010},
	url = {http://dx.doi.org/10.3233/SW-2010-0010},
	author = {Pascal Hitzler and Frank van Harmelen}
}
