@conference {8,
	title = {Conference v2.0: An uncertain version of the OAEI Conference benchmark},
	booktitle = {13th International Semantic Web Conference (ISWC 2014)},
	volume = {8797},
	year = {2014},
	month = {10/2014},
	pages = {148-163},
	publisher = {Lecture Notes in Computer Science, Springer},
	organization = {Lecture Notes in Computer Science, Springer},
	address = {Riva del Garda, Italy},
	abstract = {The Ontology Alignment Evaluation Initiative is a set of benchmarks for evaluating the performance of ontology alignment systems. In this paper we re-examine the Conference track of the OAEI, with a focus on the degree of agreement between the reference alignments within this track and the opinion of experts. We propose a new version of this benchmark that more closely corresponds to expert opinion and confidence on the matches. The performance of top alignment systems is compared on both versions of the benchmark. Additionally, a general method for crowdsourcing the development of more benchmarks of this type using Amazon{\textquoteright}s Mechanical Turk is introduced and shown to be scalable, cost-effective and to agree well with expert opinion.},
	keywords = {benchmark, OAEI, Ontology Alignment},
	author = {Michelle Cheatham and Pascal Hitzler},
	editor = {Peter Mika and Tania Tudorache and Abraham Bernstein and Chris Welty and Craig A. Knoblock and Denny Vrandecic and Paul T. Groth and Natasha F. Noy and Krzysztof Janowicz and Carole A. Goble}
}
@conference {160,
	title = {Very Large Scale OWL Reasoning through Distributed Computation},
	booktitle = {11th International Semantic Web Conference (ISWC 2012), Proceedings, Part II},
	volume = {7650},
	year = {2012},
	pages = {407{\textendash}414},
	publisher = {Springer},
	organization = {Springer},
	address = {Boston, MA, USA},
	abstract = {<p class="rtejustify">Due to recent developments in reasoning algorithms of the\&nbsp;various OWL profiles, the classification time for an ontology has come\&nbsp;down drastically. For all of the popular reasoners, in order to process\&nbsp;an ontology, an implicit assumption is that the ontology should fit in\&nbsp;primary memory. The memory requirements for a reasoner are already\&nbsp;quite high, and considering the ever increasing size of the data to be\&nbsp;processed and the goal of making reasoning Web scale, this assumption\&nbsp;becomes overly restrictive. In our work, we study several distributed\&nbsp;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.</p>
},
	keywords = {Distributed Reasoning, Ontology Classification, OWL EL},
	doi = {10.1007/978-3-642-35173-0_30},
	url = {http://dx.doi.org/10.1007/978-3-642-35173-0_30},
	author = {Raghava Mutharaju},
	editor = {Philippe Cudr{\'e}-Mauroux and Jeff Heflin and Evren Sirin and Tania Tudorache and J{\'e}r{\^o}me Euzenat and Manfred Hauswirth and Josiane Xavier Parreira and James A. Hendler and Guus Schreiber and Abraham Bernstein and Eva Blomqvist}
}
