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DTSTART:20191103T020000
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UID:calendar.361271.field_event_date.0@www.wright.edu
DTSTAMP:20260220T000921Z
CREATED:20190709T175941Z
DESCRIPTION:Ph.D. Committee:  Drs. Pascal Hitzler (advisor)\, John Gallaghe
 r\, TK Prasad\, and Karl Hammar (Jonkoping University\, Sweden) ABSTRACT:T
 he volume of Linked Data (LD) being published continues to rise tremendous
 ly. Hence the quality of LD has become of great interest to LD owners\, pu
 blishers\, engineers\, as well as users\, who look for assurances regardin
 g the trustworthiness of\, not only the data\, but also its source and reu
 se.  However\, the meaning of quality of LD is far from straightforward\, 
 and can suggest different things to different people in different contexts
 . Many recent studies have attempted to address this issue through dimensi
 ons that are internal to the dataset itself. An editorial in 2014 presents
  the principles of 5 Stars of LD Vocabulary Use. It introduces the concept
  that the quality of a linked dataset can be indirectly measured by how it
 s base/underlying vocabulary reuses\, and is being reused\, by the base vo
 cabularies of other linked datasets. The idea is that a good vocabulary sh
 ould give us a measure of how powerful\, effective\, and easy it is\, to u
 se LD and be able to restrict the potential interpretations of the classes
  and roles used in LD querying\, defining their intended purpose. This wor
 k is a study of these principles\, which help to rank a linked dataset fro
 m anywhere between zero and five Stars. It includes a manual review proces
 s that establishes the baseline results\, an automation of the ranking pro
 cess\, and a comparison of this process with other LD quality measurement 
 systems. This thesis seeks to establish that the base vocabulary of a link
 ed dataset does indeed have an impact on the quality of the dataset.
DTSTART;TZID=America/New_York:20190711T093000
DTEND;TZID=America/New_York:20190711T113000
LAST-MODIFIED:20190709T181042Z
LOCATION:405 Russ Engineering Tait Conference Room
SUMMARY:Ph.D. Dissertation Proposal Defense An Automated Assessment of Link
 ed Data Vocabularies By Stella Sam
URL;TYPE=URI:/events/phd-dissertation-proposal-defens
 e-automated-assessment-linked-data-vocabularies-stella-sam
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