Mapping Relational Databases into Ontologies through a Graph-based Formal Model

Yang, Shihan; Wu, Jinzhan

doi:10.1109/skg.2010.33

Cited by 8 publications

(6 citation statements)

References 7 publications

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“…Their approach consists of transforming the As our model is based on the idea that all the data will be converted into Semantic data hence related research work are also included. [13] Proposed W-graph, a bridge to automatically retrieve ontology from relational databases. They have adopted a medium model method i.e., to convert relational database in to a middle model and then apply ontology to this middle model.…”

Section: Related Workmentioning

confidence: 99%

An Approach for Secure Semantic Data Integration at Data as a Service (DaaS) Layer

Aftab¹,

Afzal²,

Khalid³

2015

IJIET

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Abstract-Data integration provides a uniform view of a set of heterogeneous data sources and facilitates users to query without any knowledge of the underlying heterogeneous data sources. In current era, Service Oriented Architecture and Cloud computing together has enabled users to access services over the Internet at a low cost. Cloud computing model provides a layer which is responsible for providing data to the other layers and services i.e., Data as a service (DaaS) layer. The issue of providing an integrated view of data can be handled using Semantic data; the data stored in a way that is understandable by machines and integratable without human intervention. However, integrating data using semantic web technology without enforcing any access management will raise privacy and confidentiality concerns. Different data owners store data in heterogeneous format based on their requirements. This leads to the data interoperability problem. This research proposes a framework that would allow data from different sources to be integrated using the concept of semantic data, thus resolving the issue of interoperability and also devises an access control system for defining explicit privacy constraints.Index Terms-Access control management, data as a service (DaaS), data integration, interoperability.

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Section: Related Workmentioning

confidence: 99%

An Approach for Secure Semantic Data Integration at Data as a Service (DaaS) Layer

Aftab¹,

Afzal²,

Khalid³

2015

IJIET

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“…It converts the relational database schema (or the ER Model) to an ontology by using a set of predefined transformation rules. Some works [12], [13], [14], [15], [16] map ontologies to relational databases schemata in order to maintain interoperability between them. The schema mapping is performed using mapping rules that update the ontology when the database is modified and vice-versa.…”

Section: Related Workmentioning

confidence: 99%

Transformation Rules for Building Owl Ontologies from Relational Databases

Louhdi¹,

Behja²,

Alaoui³

2013

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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“…The legacy web data in varietal forms can be classified roughly into three types: structured data, such as relational databases; semistructured data, such as XML documents and emails; and nonstructured data, such as general text and video. Recently, technologies of automatically transforming semistructured data or structured data into a domain ontology through mediate modeling are promising [2][3][4][5][6]. In these technologies, some semantic collisions appear inevitably when the same domain ontology has been built from multiple data sources.…”

Section: Introductionmentioning

confidence: 99%

“…As for semistructured data sources, in [4][5][6], researchers employ a mediate model language for modeling semistructured data and then transform middle models into the domain ontology. The validity checking has been provided 2 Journal of Applied Mathematics while collisions happen, which is generally a syntax checking.…”

Section: Introductionmentioning

confidence: 99%

Semantic Consistency Checking in Building Ontology from Heterogeneous Sources

Yang

Hui‐min

2014

Journal of Applied Mathematics

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Semantic collision is inevitable while building a domain ontology from heterogeneous data sources (semi-)automatically. Therefore, the semantic consistency is indispensable precondition for building a correct ontology. In this paper, a model-checking-based method is proposed to handle the semantic consistency problem with a kind of middle-model methodology, which could extract a domain ontology from structured and semistructured data sources semiautomatically. The method translates the middle model into the Kripke structure, and consistency assertions into CTL formulae, so a consistency checking problem is promoted to a global model checking. Moreover, the feasibility and correctness of the transformation is proved, and case studies are provided.

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Mapping Relational Databases into Ontologies through a Graph-based Formal Model

Cited by 8 publications

References 7 publications

An Approach for Secure Semantic Data Integration at Data as a Service (DaaS) Layer

An Approach for Secure Semantic Data Integration at Data as a Service (DaaS) Layer

Transformation Rules for Building Owl Ontologies from Relational Databases

Semantic Consistency Checking in Building Ontology from Heterogeneous Sources

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