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- item-1 hasContent james-p-mcglothlin.
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- item-2 hasContent latifur-khan.
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- 532 creator michael-martin.
- 532 creator sebastian-tramp.
- 532 creator soeren-auer.
- 532 creator thomas-riechert.
- 532 creator ulf-morgenstern.
- 532 type InProceedings.
- 532 label "The Catalogus Professorum Lipsiensis - Semantics-based Collaboration and Exploration for Historians".
- 532 sameAs 532.
- 532 abstract "The World Wide Web (WWW), as an ubiquitous medium for publication and exchange, already significantly influenced the way how historians work: the availability of public catalogs and bibliographies enable efficient research of relevant content for a certain investigation; the increasing digitization of works from historical archives and libraries, in addition, enables historians to directly access historical sources remotely. The capabilities of the WWW as a medium for collaboration, however, are only starting to be explored. Many historical questions are only answerable by combining information from different sources, from different researchers and organizations. Furthermore, after analyzing original sources, the derived information is often more comprehensive than can be captured by simple keyword indexing. In [3] we report about the application of an adaptive, semantics-based knowledge engineering approach for the development of a prosopographical knowledge base. In this demonstration we will showcase the comprehensive prosopographical knowledge base and its potential for applications. In prosopographical research, historians analyze common characteristics of historical groups by studying statistically relevant quantities of individual biographies. Untraceable periods of biographies can be determined on the basis of such accomplished analyses in combination with statistically examinations as well as patterns of relationships between individuals and their activities. In our case, researchers from the Historical Seminar at the University of Leipzig aimed at creating a prosopographical knowledge base about the life and work of professors in the 600 years history of University of Leipzig ranging from the year 1409 till 2009 - the Catalogus Professorum Lipsiensis (CPL).".
- 532 hasAuthorList authorList.
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- 532 title "The Catalogus Professorum Lipsiensis - Semantics-based Collaboration and Exploration for Historians".
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- 533 creator gregor-leban.
- 533 creator marko-grobelnik.
- 533 type InProceedings.
- 533 label "Displaying email-related contextual information using Contextify".
- 533 sameAs 533.
- 533 abstract "Contextify is a tool for maximizing user productivity by showing email-related contextual information. The contextual information is determined based on the currently selected email and includes related emails, people, attachments and web links. This content is displayed in a sidebar in Microsoft Outlook and in a special dialog that can display an extended context.".
- 533 hasAuthorList authorList.
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- 533 keyword "email".
- 533 keyword "optimization".
- 533 keyword "productivity".
- 533 keyword "visualization".
- 533 title "Displaying email-related contextual information using Contextify".
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- item-1 hasContent gregor-leban.
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- 534 creator ming-mao.
- 534 creator paul-munro.
- 534 creator yefei-peng.
- 534 type InProceedings.
- 534 label "Ontology Mapping Neural Network: An Approach to Learning and Inferring Correspondences among Ontologies".
- 534 sameAs 534.
- 534 abstract "An ontology mapping neural network (OMNN) is proposed in order to learn and infer correspondences among ontologies. It extends the Identical Elements Neural Network (IENN)'s ability to represent and map complex relationships. The learning dynamics of simultaneous (interlaced) training of similar tasks interact at the shared connections of the networks. The output of one network in response to a stimulus to another network can be interpreted as an analogical mapping. In a similar fashion, the networks can be explicitly trained to map specific items in one domain to specific items in another domain. Representation layer helps the network learn relationship mapping with direct training method. OMNN is applied to several OAEI benchmark test cases to test its performance on ontology mapping. Results show that OMNN approach is competitive to the top performing systems that participated in OAEI 2009.".
- 534 hasAuthorList authorList.
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- 534 keyword "analogy".
- 534 keyword "learning".
- 534 keyword "neural network".
- 534 keyword "ontology mapping".
- 534 title "Ontology Mapping Neural Network: An Approach to Learning and Inferring Correspondences among Ontologies".
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- item-1 hasContent yefei-peng.
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- 535 creator mihai-radulescu.
- 535 creator rolf-sint.