00643nas a2200169 4500008004100000245008300041210006900124260001200193653002500205653001900230653002400249653002000273100001800293700002000311700002300331856011900354 2017 eng d00a Propositional rule extraction from neural networks under background knowledge0 aPropositional rule extraction from neural networks under backgro c07/201710aBackground knowledge10aNeural Network10aPropositional Logic10aRule Extraction1 aLabaf, Maryam1 aHitzler, Pascal1 aEvans, Anthony, B. uhttps://daselab.cs.ksu.edu/publications/propositional-rule-extraction-neural-networks-under-background-knowledge-001738nas a2200301 4500008004100000245007500041210006900116260008000185300001200265490000900277520074900286653001401035653000901049653002301058100002301081700002001104700001601124700002101140700002301161700001701184700002401201700002101225700002001246700002101266700002401287700002201311856010301333 2014 eng d00aConference v2.0: An uncertain version of the OAEI Conference benchmark0 aConference v20 An uncertain version of the OAEI Conference bench aRiva del Garda, ItalybLecture Notes in Computer Science, Springerc10/2014 a148-1630 v87973 aThe 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’s Mechanical Turk is introduced and shown to be scalable, cost-effective and to agree well with expert opinion.10abenchmark10aOAEI10aOntology Alignment1 aCheatham, Michelle1 aHitzler, Pascal1 aMika, Peter1 aTudorache, Tania1 aBernstein, Abraham1 aWelty, Chris1 aKnoblock, Craig, A.1 aVrandecic, Denny1 aGroth, Paul, T.1 aNoy, Natasha, F.1 aJanowicz, Krzysztof1 aGoble, Carole, A. uhttps://daselab.cs.ksu.edu/publications/conference-v20-uncertain-version-oaei-conference-benchmark00546nas a2200169 4500008004100000245006900041210006600110300001400176653001700190653002300207653002900230653000800259100001800267700002400285700002000309856004700329 2012 eng d00aA logical geo-ontology design pattern for quantifying over types0 alogical geoontology design pattern for quantifying over types a239–24810aBiodiversity10adescription logics10aOntology Design Patterns10aOWL1 aCarral, David1 aJanowicz, Krzysztof1 aHitzler, Pascal uhttp://doi.acm.org/10.1145/2424321.242435202649nas a2200277 4500008004100000245009100041210006900132260003400201300001400235490000900249520174100258653003601999653001902035653003002054653003702084653002202121653002002143100002102163700002502184700002002209700001602229700003202245700001902277700002402296856005102320 2010 eng d00aProvenance Context Entity (PaCE): Scalable Provenance Tracking for Scientific RDF Data0 aProvenance Context Entity PaCE Scalable Provenance Tracking for aHeidelberg, GermanybSpringer a461–4700 v61873 a
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.
10aBiomedical knowledge repository10aContext theory10aProvenance context entity10aProvenance Management Framework.10aProvenir ontology10aRDF reification1 aSahoo, Satya, S.1 aBodenreider, Olivier1 aHitzler, Pascal1 aSheth, Amit1 aThirunarayan, Krishnaprasad1 aGertz, Michael1 aLudäscher, Bertram uhttp://dx.doi.org/10.1007/978-3-642-13818-8_32