TY - JOUR T1 - Semantics for Big Data JF - AI Magazine Y1 - 2015 A1 - Frank van Harmelen A1 - James A. Hendler A1 - Pascal Hitzler A1 - Krzysztof Janowicz VL - 36 UR - http://www.aaai.org/ojs/index.php/aimagazine/article/view/2559 ER - TY - JOUR T1 - Why the Data Train Needs Semantic Rails JF - AI Magazine Y1 - 2015 A1 - Krzysztof Janowicz A1 - Frank van Harmelen A1 - James A. Hendler A1 - Pascal Hitzler AB - 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. VL - 36 UR - http://www.aaai.org/ojs/index.php/aimagazine/article/view/2560 ER - TY - JOUR T1 - Reports on the 2013 AAAI Fall Symposium Series JF - AI Magazine Y1 - 2014 A1 - Gully Burns A1 - Yolanda Gil A1 - Yan Liu A1 - Natalia Villanueva-Rosales A1 - Sebastian Risi A1 - Joel Lehman A1 - Jeff Clune A1 - Christian Lebiere A1 - Paul S. Rosenbloom A1 - Frank van Harmelen A1 - James A. Hendler A1 - Pascal Hitzler A1 - Krzysztof Janowicz A1 - Samarth Swarup VL - 35 UR - http://www.aaai.org/ojs/index.php/aimagazine/article/view/2538 ER - TY - CONF T1 - Crowdsourcing Semantics for Big Data in Geoscience Applications T2 - Semantics for Big Data: Papers from the AAAI Symposium Y1 - 2013 A1 - Tom Narock A1 - Pascal Hitzler ED - Frank van Harmelen ED - Jim Hendler ED - Pascal Hitzler ED - Krzysztof Janowicz JF - Semantics for Big Data: Papers from the AAAI Symposium CY - Arlington, Virginia ER - TY - Generic T1 - Semantics for Big Data: Papers from the AAAI Symposium, November 15-17, 2013, Arlington, Virginia T2 - AAAI Symposium on Semantics for Big Data Y1 - 2013 ED - Frank van Harmelen ED - James A. Hendler ED - Pascal Hitzler ED - Krzysztof Janowicz JF - AAAI Symposium on Semantics for Big Data CY - Arlington, Virginia, USA ER - TY - JOUR T1 - Cognitive Approaches for the Semantic Web (Dagstuhl Seminar 12221) JF - Dagstuhl Reports Y1 - 2012 A1 - Dedre Gentner A1 - Frank van Harmelen A1 - Pascal Hitzler A1 - Krzysztof Janowicz A1 - Kai-Uwe Kühnberger VL - 2 UR - http://dx.doi.org/10.4230/DagRep.2.5.93 ER - TY - JOUR T1 - Preface - Special issue on commonsense reasoning for the semantic web JF - Annals of Mathematics and Artificial Intelligence Y1 - 2010 A1 - Frank van Harmelen A1 - Andreas Herzig A1 - Pascal Hitzler A1 - Guilin Qi VL - 58 UR - http://dx.doi.org/10.1007/s10472-010-9209-7 ER - TY - JOUR T1 - A Reasonable Semantic Web JF - Semantic Web Y1 - 2010 A1 - Pascal Hitzler A1 - Frank van Harmelen KW - Automated Reasoning KW - Formal Semantics KW - Knowledge representation KW - Linked Open Data KW - Semantic Web AB -
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.
VL - 1 UR - http://dx.doi.org/10.3233/SW-2010-0010 ER -