Mapping between Relational Database Schema and OWL Ontology for Deep Annotation

Xu, Zhuoming; Zhang, Shichao; Dong, Yuan

doi:10.1109/wi.2006.114

Cited by 66 publications

(34 citation statements)

References 6 publications

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“…Our study demonstrates a feasible approach to incorporate both structured and unstructured data while creating a machine-readable fashion for data representation. Along with this approach, future efforts should include mapping strategies to merge relational data that are used for representing structured data with ontologies (Cullot, Ghawi, & Yétongnon, 2007;Xu, Zhang, & Dong, 2006).…”

Section: Discussionmentioning

confidence: 99%

Knowledge Representation in Patient Safety Reporting: An Ontological Approach

Liang

Gong

2016

Journal of Data and Information Science

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Purpose: The current development of patient safety reporting systems is criticized for loss of information and low data quality due to the lack of a uniformed domain knowledge base and text processing functionality. To improve patient safety reporting, the present paper suggests an ontological representation of patient safety knowledge.Design/methodology/approach: We propose a framework for constructing an ontological knowledge base of patient safety. The present paper describes our design, implementation, and evaluation of the ontology at its initial stage. Findings:We describe the design and initial outcomes of the ontology implementation. The evaluation results demonstrate the clinical validity of the ontology by a self-developed survey measurement. Research limitations:The proposed ontology was developed and evaluated using a small number of information sources. Presently, US data are used, but they are not essential for the ultimate structure of the ontology. Practical implications:The goal of improving patient safety can be aided through investigating patient safety reports and providing actionable knowledge to clinical practitioners. As such, constructing a domain specific ontology for patient safety reports serves as a cornerstone in information collection and text mining methods.Originality/value: The use of ontologies provides abstracted representation of semantic information and enables a wealth of applications in a reporting system. Therefore, constructing such a knowledge base is recognized as a high priority in health care.

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Section: Discussionmentioning

confidence: 99%

Knowledge Representation in Patient Safety Reporting: An Ontological Approach

Liang

Gong

2016

Journal of Data and Information Science

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“…Ontology is not a database schema, but a simplification is also possible: an ontology for the repositories of knowledge is what the diagram of entity is for a database; it is a schema, a model of a certain field of knowledge, readable by both computers and people. Creating an unified, formal language of related-concepts definition and its further sharing with a number of users is a first and most important goal of ontologies [33][34][35].…”

Section: Ontologymentioning

confidence: 99%

Formalization of Technological Knowledge in the Field of Metallurgy using Document Classification Tools Supported with Semantic Techniques

Regulski

2017

Archives of Metallurgy and Materials

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The process of knowledge formalization is an essential part of decision support systems development. Creating a technological knowledge base in the field of metallurgy encountered problems in acquisition and codifying reusable computer artifacts based on text documents. The aim of the work was to adapt the algorithms for classification of documents and to develop a method of semantic integration of a created repository. Author used artificial intelligence tools: latent semantic indexing, rough sets, association rules learning and ontologies as a tool for integration. The developed methodology allowed for the creation of semantic knowledge base on the basis of documents in natural language in the field of metallurgy.

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“…Where DB2OWL is used to create new ontology from existing schema, D2OMapper (Xu et al, 2006) is a tool for automatic or semi-automatic creation of the mappings between database schema and existing ontology. D2OMapper follows a set of predefined heuristic rules, based on the conceptual correspondences between the schema and ontology.…”

Section: State Of the Art In Database To Ontology Mappingmentioning

confidence: 99%

Local Ontologies for Semantic Interoperability in Supply Chain Networks

2011

Proceedings of the 13th International Conference on Enterprise Information Systems

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Mapping between Relational Database Schema and OWL Ontology for Deep Annotation

Cited by 66 publications

References 6 publications

Knowledge Representation in Patient Safety Reporting: An Ontological Approach

Knowledge Representation in Patient Safety Reporting: An Ontological Approach

Formalization of Technological Knowledge in the Field of Metallurgy using Document Classification Tools Supported with Semantic Techniques

Local Ontologies for Semantic Interoperability in Supply Chain Networks

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