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Назив: Semantic integration of enterprise information systems using meta-metadata ontology
Аутори: Cverdelj-Fogaraši, Igor 
Sladić, Goran 
Gostojić, Stevan 
Segedinac, Milan 
Milosavljević, Branko 
Датум издавања: 1-мај-2017
Часопис: Information Systems and e-Business Management
Сажетак: © 2016, Springer-Verlag Berlin Heidelberg. This paper proposes a non-domain-specific metadata ontology as a core component in a semantic model-based document management system (DMS), a potential contender towards the enterprise information systems of the next generation. What we developed is the core semantic component of an ontology-driven DMS, providing a robust semantic base for describing documents’ metadata. We also enabled semantic services such as automated semantic translation of metadata from one domain to another. The core semantic base consists of three semantic layers, each one serving a different view of documents’ metadata. The core semantic component’s base layer represents a non-domain-specific metadata ontology founded on ebRIM specification. The main purpose of this ontology is to serve as a meta-metadata ontology for other domain-specific metadata ontologies. The base semantic layer provides a generic metadata view. For the sake of enabling domain-specific views of documents’ metadata, we implemented two domain-specific metadata ontologies, semantically layered on top of ebRIM, serving domain-specific views of the metadata. In order to enable semantic translation of metadata from one domain to another, we established model-to-model mappings between these semantic layers by introducing SWRL rules. Having the semantic translation of metadata automated not only allows for effortless switching between different metadata views, but also opens the door for automating the process of documents long-term archiving. For the case study, we chose judicial domain as a promising ground for improving the efficiency of the judiciary by introducing the semantics in this field.
URI: https://open.uns.ac.rs/handle/123456789/4182
ISSN: 16179846
DOI: 10.1007/s10257-015-0303-6
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