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Semantics based customization of UBL document schemas

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Abstract

Universal Business Language (UBL) is an OASIS initiative to develop common business document schemas to provide document interoperability in the eBusiness domain. Since the data requirements change according to a context, UBL schemas need to be customized and UBL defines a guideline to be followed for customization of schemas. XSD derivation based customization as proposed by UBL provides syntactic interoperability, that is, an XML parser that can interpret standard UBL documents can also interpret customized UBL documents. We argue that for UBL to become mainstream, syntactic interoperability alone is not enough. It needs to be supported by semantic interoperability, that is, it must be possible for users and even automated processes to discover and reuse customizations provided by other users.

In this paper, we describe how to improve the UBL customization mechanism by providing semantic representations for context domains and describe how these semantics can be utilized by automated processes for component discovery and schema customization. For this purpose, we derive ontologies from taxonomies like the North American Industry Classification System (NAICS), the Universal Standard Products and Services Classification (UNSPSC) and relate corresponding concepts from different ontologies through ontology alignment. Then, we process these aligned ontologies using a reasoner to compute inferred ontologies representing context domains. We show that when custom UBL components are annotated using classes from these ontologies, automated discovery and customization becomes possible.

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Correspondence to Yalin Yarimagan.

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Communicated by Ahmed K. Elmagarmid

This work is supported in part by the European Commission, Project No: IST-027306-STP ABILITIES and the Scientific and Technical Research Council of Turkey (TÜBÍTAK), METU-ISTEC Project, No: EEEAG 105E068.

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Yarimagan, Y., Dogac, A. Semantics based customization of UBL document schemas. Distrib Parallel Databases 22, 107–131 (2007). https://doi.org/10.1007/s10619-007-7014-z

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