A case study of linked enterprise data
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 6497 LNCS, Issue: PART 2, Page: 129-144
2010
- 10Citations
- 66Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Conference Paper Description
Even though its adoption in the enterprise environment lags behind the public domain, semantic (web) technologies, more recently the linked data initiative, started to penetrate into business domain with more and more people recognising the benefit of such technologies. An evident advantage of leveraging semantic technologies is the integration of distributed data sets that benefit companies with a great return of value. Enterprise data, however, present significantly different characteristics from public data on the Internet. These differences are evident in both technical and managerial perspectives. This paper reports a pilot study, carried out in an international organisation, aiming to provide a collaborative workspace for fast and low-overhead data sharing and integration. We believe that the design considerations, study outcomes, and learnt lessons can help making decisions of whether and how one should adopt semantic technologies in similar contexts. © 2010 Springer-Verlag.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=78650909079&origin=inward; http://dx.doi.org/10.1007/978-3-642-17749-1_9; http://link.springer.com/10.1007/978-3-642-17749-1_9; http://link.springer.com/content/pdf/10.1007/978-3-642-17749-1_9; https://dx.doi.org/10.1007/978-3-642-17749-1_9; https://link.springer.com/chapter/10.1007/978-3-642-17749-1_9
Springer Science and Business Media LLC
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