By Michel Klien (Editor) Borys Omelayenko (Editor)
Lately the realm of the Semantic net has obtained major value inside of either academia and industries. in preference to the present net essentially facing textual details, the Semantic net has to house conceptual and data versions. those versions must be translated among varied languages, representations, and modeling paradigms to let worldwide version seek, retrieval and composition. The ebook covers major concerns in remodeling overwhelming majority of versions for use within the context of the Semantic net: XML schemas, relational versions, UML diagrams, RDF schemas, and ontologies. diversified sensible techniques are offered within the ebook in addition to discussions on a few theoretical matters that come up alongside the way in which.
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Extra resources for Knowledge Transformation for the Semantic Web
If a relation rl corresponds to a relationship rs, then (C) both have the same name and EITHER (D) a relation attribute ra comes from a relationship end rse such that (E) the name of the attribute ra is equal to the name of the relationship end rse followed by underscore followed by the name of a key attribute rsek of the referenced entity of the relationship end, (F) the attribute ra has the same type as key attribute rsek, and (G) attribute ra is part of the key of relation rl, OR (H) a relation attribute ra comes from a relationship attribute rsa with the same name, type, and attribute ra is a non-key attribute in the relation.
Workshop on the Semantic Web at the 4th European Conference on Research and Advanced Technology for Digital Libraries (SemWeb). com Tool for Intelligent Conceptual Modelling. In: Proc. 7th Intl. Workshop on Knowledge Representation meets Databases (KRDB'OO). : Metamodel Transformation of Data Models. : Proc. UML'2002 Workshop in Software Model Engineering (WiSME 2002). metamodel. com/wisme-2002 (2002) 34 Knowledge Transformation for the Semantic Web B. Omelayenko and M. ogi. edu Abstract. Data models use a limited number of basic structures to represent data such as collections, attribute-value pairs, and scalars.
2. Thus, combining patterns and constraintbased rule derivations can ease the task of rule specification, both of which are enabled by the uniform framework. We intend to investigate such higher-level transformation rules further. 5 Related Work This chapter extends our earlier work [4, 5], which introduced the use of a uniform, RDFbased representation scheme allowing model, schema, and instance data to be represented using triples. The metamodel introduced here is significantly more complex and has a relatively complete set of structural primitives, including typical database modeling constructs: struct (similar to tuple) and explicit collection primitives set, bag, and list.