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 a2200157 4500008004100000245006800041210006800109260003300177100001600210700002000226700002400246700001700270700002000287700002400307856010100331 2013 eng d00aCrowdsourcing Semantics for Big Data in Geoscience Applications0 aCrowdsourcing Semantics for Big Data in Geoscience Applications aArlington, Virginiac11/20131 aNarock, Tom1 aHitzler, Pascal1 avan Harmelen, Frank1 aHendler, Jim1 aHitzler, Pascal1 aJanowicz, Krzysztof uhttps://daselab.cs.ksu.edu/publications/crowdsourcing-semantics-big-data-geoscience-applications00590nas 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-virginia00514nas a2200157 4500008004100000245007100041210006900112300001300181490000600194100001900200700002400219700002000243700002400263700002500287856004400312 2012 eng d00aCognitive Approaches for the Semantic Web (Dagstuhl Seminar 12221)0 aCognitive Approaches for the Semantic Web Dagstuhl Seminar 12221 a93–1160 v21 aGentner, Dedre1 avan Harmelen, Frank1 aHitzler, Pascal1 aJanowicz, Krzysztof1 aKühnberger, Kai-Uwe uhttp://dx.doi.org/10.4230/DagRep.2.5.9300474nas a2200145 4500008004100000245007400041210006900115300001000184490000700194100002400201700002000225700002000245700001500265856004800280 2010 eng d00aPreface - Special issue on commonsense reasoning for the semantic web0 aPreface Special issue on commonsense reasoning for the semantic a1–20 v581 avan Harmelen, Frank1 aHerzig, Andreas1 aHitzler, Pascal1 aQi, Guilin uhttp://dx.doi.org/10.1007/s10472-010-9209-701540nas a2200193 4500008004100000245003000041210002800071300001200099490000600111520103000117653002401147653002101171653002901192653002101221653001701242100002001259700002401279856004301303 2010 eng d00aA Reasonable Semantic Web0 aReasonable Semantic Web a39–440 v13 a
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.
10aAutomated Reasoning10aFormal Semantics10aKnowledge representation10aLinked Open Data10aSemantic Web1 aHitzler, Pascal1 avan Harmelen, Frank uhttp://dx.doi.org/10.3233/SW-2010-0010