00416nas a2200145 4500008004100000245002700041210002700068300001000095490000700105100002400112700002300136700002000159700002400179856006700203 2015 eng d00aSemantics for Big Data0 aSemantics for Big Data a3–40 v361 avan Harmelen, Frank1 aHendler, James, A.1 aHitzler, Pascal1 aJanowicz, Krzysztof uhttp://www.aaai.org/ojs/index.php/aimagazine/article/view/255901741nas a2200157 4500008004100000245004400041210004400085300001100129490000700140520127800147100002401425700002401449700002301473700002001496856006701516 2015 eng d00aWhy the Data Train Needs Semantic Rails0 aWhy the Data Train Needs Semantic Rails a5–140 v363 aWhile 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.1 aJanowicz, Krzysztof1 avan Harmelen, Frank1 aHendler, James, A.1 aHitzler, Pascal uhttp://www.aaai.org/ojs/index.php/aimagazine/article/view/256000786nas a2200265 4500008004100000245005100041210005100092300001200143490000700155100001700162700001700179700001300196700003200209700002000241700001700261700001600278700002300294700002500317700002400342700002300366700002000389700002400409700002000433856006700453 2014 eng d00aReports on the 2013 AAAI Fall Symposium Series0 aReports on the 2013 AAAI Fall Symposium Series a69–740 v351 aBurns, Gully1 aGil, Yolanda1 aLiu, Yan1 aVillanueva-Rosales, Natalia1 aRisi, Sebastian1 aLehman, Joel1 aClune, Jeff1 aLebiere, Christian1 aRosenbloom, Paul, S.1 avan Harmelen, Frank1 aHendler, James, A.1 aHitzler, Pascal1 aJanowicz, Krzysztof1 aSwarup, Samarth uhttp://www.aaai.org/ojs/index.php/aimagazine/article/view/253800590nas a2200133 4500008004100000245010200041210006900143260002900212100002400241700002300265700002000288700002400308856012400332 2013 eng d00aSemantics for Big Data: Papers from the AAAI Symposium, November 15-17, 2013, Arlington, Virginia0 aSemantics for Big Data Papers from the AAAI Symposium November 1 aArlington, Virginia, USA1 avan Harmelen, Frank1 aHendler, James, A.1 aHitzler, Pascal1 aJanowicz, Krzysztof uhttps://daselab.cs.ksu.edu/publications/semantics-big-data-papers-aaai-symposium-november-15-17-2013-arlington-virginia01724nas a2200301 4500008004100000245006700041210006700108260003000175300001400205490000900219520081100228653002601039653002801065653001101093100002301104700002901127700001701156700001701173700002101190700002201211700002301233700003001256700002301286700002001309700002301329700001901352856005101371 2012 eng d00aVery Large Scale OWL Reasoning through Distributed Computation0 aVery Large Scale OWL Reasoning through Distributed Computation aBoston, MA, USAbSpringer a407–4140 v76503 a

Due to recent developments in reasoning algorithms of the various OWL profiles, the classification time for an ontology has come down drastically. For all of the popular reasoners, in order to process an ontology, an implicit assumption is that the ontology should fit in primary memory. The memory requirements for a reasoner are already quite high, and considering the ever increasing size of the data to be processed and the goal of making reasoning Web scale, this assumption becomes overly restrictive. In our work, we study several distributed 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.

10aDistributed Reasoning10aOntology Classification10aOWL EL1 aMutharaju, Raghava1 aCudré-Mauroux, Philippe1 aHeflin, Jeff1 aSirin, Evren1 aTudorache, Tania1 aEuzenat, Jérôme1 aHauswirth, Manfred1 aParreira, Josiane, Xavier1 aHendler, James, A.1 aSchreiber, Guus1 aBernstein, Abraham1 aBlomqvist, Eva uhttp://dx.doi.org/10.1007/978-3-642-35173-0_30