By Enrico Motta, Nigel Shadbolt, Arthur Stutt, Nick Gibbins
This booklet constitutes the refereed lawsuits of the 14th overseas convention on wisdom Engineering and data administration, EKAW 2004, held in Whittleburg corridor, united kingdom in October 2004.
The 30 revised complete papers and 21 revised brief papers have been conscientiously reviewed and chosen from a variety of submissions. The papers are equipped in topical sections on ontologies: mappings and translations; ontologies: difficulties and functions; ontologies: belief and e-learning; ontology upkeep; purposes to drugs; portals; wisdom acquisition; internet providers and challenge fixing; and looking out, searching, and information acquisition.
Read or Download Engineering Knowledge in the Age of the Semantic Web: 14th International Conference, EKAW 2004, Whittlebury Hall, UK, October 5-8, 2004. Proceedings PDF
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Extra resources for Engineering Knowledge in the Age of the Semantic Web: 14th International Conference, EKAW 2004, Whittlebury Hall, UK, October 5-8, 2004. Proceedings
Rankings are common scales for the matchers, regardless of the algorithms the matchers use or the scale of the similarity values the matchers return. The Borda Count method uses the rankings of match pairs computed by the matchers as input and outputs an real number (score) associated with every match pair as output. The real number can be used to construct a combined ranking of match pairs. Basically, the Borda Count for a match pair is the sum of the number of pairs ranked below it by each matcher.
2. The PROMPT tab  is a tool for ontology merging and alignment with Prot´eg´e. As it already supports the main methodology and interaction cycle of framework, we used it as a backbone for our implementation. 3. The classification and document similarity procedures for the instance–based matching were realized on top of the NextBot engine that is the core of ProFiler, a commercial tool for information organization and retrieval by brainbot technolgies AG8 . 1 Basic Algorithms In the following, we briefly sketch how the elements of the framework were instantiated.
Aggregation of Match Results. Typically, the output of the matching algorithms is ambiguous. Term–based matchers for example may conclude that class “University” from ontology A matches class “University” from ontology B while a structure-based matcher does not assign a high similarity value to this pair, perhaps because when taking the ontologies’ structures into consideration it becomes clear that class “University” of ontology A means a set of buildings while in the context of ontology B, the class “University” denotes an organizational structure.
Engineering Knowledge in the Age of the Semantic Web: 14th International Conference, EKAW 2004, Whittlebury Hall, UK, October 5-8, 2004. Proceedings by Enrico Motta, Nigel Shadbolt, Arthur Stutt, Nick Gibbins