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dc.contributor.authorDing, Li
dc.contributor.authorTao, Jiao
dc.contributor.authorMcGuinness, Deborah L.
dc.date.accessioned2022-02-18T02:39:43Z
dc.date.available2022-02-18T02:39:43Z
dc.date.issued2008-04-01
dc.identifier.other288
dc.identifier.urihttp://archive.tw.rpi.edu/media/latest/owled2008dc_paper_25.pdf
dc.identifier.urihttps://hdl.handle.net/20.500.13015/4690
dc.description.abstractAs more applications are depending on semantic web data from diverse sources, semantic web data evaluation is becoming more critical. While language validators and general reasoners can help, these typically focus on syntax and logical consistency. Many applications need additional support for finding possible issues (as well as provable mistakes). We are investigating methods and environments that provide computational support for identifying possible problems with instance data. We report on a line of work focusing on evaluation of provable and possible problems with OWL instance data and provide some discussion motivated by our first application setting validating large amounts of diverse explanation data.
dc.relation.urihttps://tw.rpi.edu/project/InferenceWeb
dc.subjectInference Web
dc.titleOWL Instance Data Evaluation


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