@conference {608,
	title = { Propositional rule extraction from neural networks under background knowledge},
	booktitle = {Twelfth International Workshop on Neural-Symbolic Learning and Reasoning},
	year = {2017},
	month = {07/2017},
	keywords = {Background knowledge, Neural Network, Propositional Logic, Rule Extraction},
	author = {Maryam Labaf},
	editor = {Pascal Hitzler}
}
@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 {126,
	title = {A logical geo-ontology design pattern for quantifying over types},
	booktitle = {SIGSPATIAL 2012 International Conference on Advances in Geographic Information Systems (formerly known as GIS), SIGSPATIAL{\textquoteright}12, Redondo Beach, CA, USA, November 7-9, 2012},
	year = {2012},
	pages = {239{\textendash}248},
	keywords = {Biodiversity, description logics, Ontology Design Patterns, OWL},
	doi = {10.1145/2424321.2424352},
	url = {http://doi.acm.org/10.1145/2424321.2424352},
	author = {David Carral and Krzysztof Janowicz and Pascal Hitzler}
}
@conference {112,
	title = {Provenance Context Entity (PaCE): Scalable Provenance Tracking for Scientific RDF Data},
	booktitle = {Scientific and Statistical Database Management, 22nd International Conference, SSDBM 2010},
	volume = {6187},
	year = {2010},
	pages = {461{\textendash}470},
	publisher = {Springer},
	organization = {Springer},
	address = {Heidelberg, Germany},
	abstract = {<p class="rtejustify">The Semantic Web Resource Description Framework (RDF) format is being used by a large number of scientific applications to store and disseminate their datasets. The provenance information, describing the source or lineage of the datasets, is playing an increasingly significant role in ensuring data quality, computing trust value of the datasets, and ranking query results. Current Semantic Web provenance tracking approaches using the RDF reification vocabulary suffer from a number of known issues, including lack of formal semantics, use of blank nodes, and application-dependent interpretation of reified RDF triples that hinders data sharing. In this paper, we introduce a new approach called Provenance Context Entity (PaCE) that uses the notion of provenance context to create provenance-aware RDF triples without the use of RDF reification or blank nodes. We also define the formal semantics of PaCE through a simple extension of the existing RDF(S) semantics that ensures compatibility of PaCE with existing Semantic Web tools and implementations. We have implemented the PaCE approach in the Biomedical Knowledge Repository (BKR) project at the US National Library of Medicine to support provenance tracking on RDF data extracted from multiple sources, including biomedical literature and the UMLS Metathesaurus. The evaluations demonstrate a minimum of 49\% reduction in total number of provenancespecific RDF triples generated using the PaCE approach as compared to RDF reification. In addition, using the PACE approach improves the performance of complex provenance queries by three orders of magnitude and remains comparable to the RDF reification approach for simpler provenance queries.\&nbsp;</p>
},
	keywords = {Biomedical knowledge repository, Context theory, Provenance context entity, Provenance Management Framework., Provenir ontology, RDF reification},
	doi = {10.1007/978-3-642-13818-8_32},
	url = {http://dx.doi.org/10.1007/978-3-642-13818-8_32},
	author = {Satya S. Sahoo and Olivier Bodenreider and Pascal Hitzler and Amit Sheth and Krishnaprasad Thirunarayan},
	editor = {Michael Gertz and Bertram Lud{\"a}scher}
}
