Integrating Relational Databases with the Semantic Web: A Reflection

Sequeda, Juan F.

doi:10.1007/978-3-319-61033-7_4

Cited by 6 publications

(5 citation statements)

References 49 publications

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“…The proposed model is based on a materialization-based approach (forward chaining) [7]. In the materialization-base approach [28], the database D is the input, O is the target ontology and the mapping from D to O is M. The legacy data source is the ABox (A) and the ontology is the TBox (T). The SPARQL query Q is executed over the D, O, and M. Therefore, the access to the underlying data sources is abstracted independent of the mapping.…”

Section: Maritimementioning

confidence: 99%

“…For this purpose, a use case for a hospital domain is performed. In the scope of the use case, the steps adhered are as follows: a hospital database is created, a hospital ontology is developed, a hospital policy ontology is created for the related hospital ontology, the relevant mappings between the policy ontology and the created database are established by utilizing the Ontop framework [28], and various queries are written using Ontop SPARQL. Ontop is an open-source platform to query databases through ontologies.…”

Section: Integrating Obac With Obda: a Use Casementioning

confidence: 99%

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Ontology Based Access Control: A Case Study through Ontology Based Data Access

Can

Ünalır²

2023

DEUFMD

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Ontology Based Data Access (OBDA) is the provision of data access and data integration as a result of the mapping that is established between an ontology and a data source. Thus, storing large amounts of data becomes easier, more powerful queries can be written, and management of complex information systems can be performed quickly and effectively by using Semantic Web technologies. Ontology Based Access Control (OBAC) uses Semantic Web technologies to enable the enforcement of access control mechanism. Therefore, only authorized persons can access data to protect data privacy. In this study, OBDA and OBAC are integrated to improve security while providing data virtualization with a data model-independent access control approach. Therefore, a use case study for the healthcare domain is presented. Hence, a relational database for the hospital domain, a Hospital Ontology for the related hospital database and an access control policy are created. Also, the relevant mappings between the hospital database and the Hospital Ontology are established by using the Ontop framework and finally, various queries are executed by using Ontop SPARQL to evaluate mappings and access rules.

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Section: Maritimementioning

confidence: 99%

Section: Integrating Obac With Obda: a Use Casementioning

confidence: 99%

Ontology Based Access Control: A Case Study through Ontology Based Data Access

Can

Ünalır²

2023

DEUFMD

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“…Traditionally, the basic elements of relational database, i.e., database schema R, constraints Σ over R, instance I of R, could be mapped into RDF graph and OWL by using direct mapping M. As we mentioned earlier, the M is defined based on the set of datalog predicates and rules, in which a lot of experience and effort from domain experts are required. We could say a direct mapping M is semantic preserving mapping [23], namely, there is no semantic inconsistency between RDB and OWL, if for every database schema R, set of constraints Σ (PKs and FKs), instance I of R, and semantics of the ontology Σ O , which satisfies:…”

Section: Problem Statementmentioning

confidence: 99%

A Semi-Automatic Semantic Consistency-Checking Method for Learning Ontology from Relational Database

Molnár

Benczúr

2021

Information

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To tackle the issues of semantic collision and inconsistencies between ontologies and the original data model while learning ontology from relational database (RDB), a semi-automatic semantic consistency checking method based on graph intermediate representation and model checking is presented. Initially, the W-Graph, as an intermediate model between databases and ontologies, was utilized to formalize the semantic correspondences between databases and ontologies, which were then transformed into the Kripke structure and eventually encoded with the SMV program. Meanwhile, description logics (DLs) were employed to formalize the semantic specifications of the learned ontologies, since the OWL DL showed good semantic compatibility and the DLs presented an excellent expressivity. Thereafter, the specifications were converted into a computer tree logic (CTL) formula to improve machine readability. Furthermore, the task of checking semantic consistency could be converted into a global model checking problem that could be solved automatically by the symbolic model checker. Moreover, an example is given to demonstrate the specific process of formalizing and checking the semantic consistency between learned ontologies and RDB, and a verification experiment was conducted to verify the feasibility of the presented method. The results showed that the presented method could correctly check and identify the different kinds of inconsistencies between learned ontologies and its original data model.

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“…Diverse mapping languages for transforming relational data into RDF have been introduced, reported in 2009 for the first time as a survey by W3C incubator group. Sequeda et al explain the limitations of semantic technologies in relational databases integration in [11]. During the recent years several extension to R2RML have been proposed in order to represent mapping rules such as RML [4] by Dimou et al or D2RML [2] by Chortaras et al The same applies for the implementation of tools to execute mapping rules in different languages.…”

Section: Related Workmentioning

confidence: 99%

MapSDI: A Scaled-Up Semantic Data Integration Framework for Knowledge Graph Creation

Jozashoori

Vidal

2019

Lecture Notes in Computer Science

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Semantic web technologies have significantly contributed with effective solutions for the problems of data integration and knowledge graph creation. However, with the rapid growth of big data in diverse domains, different interoperability issues still demand to be addressed, being scalability one of the main challenges. In this paper, we address the problem of knowledge graph creation at scale and provide MapSDI, a mapping rule-based framework for optimizing semantic data integration into knowledge graphs. MapSDI allows for the semantic enrichment of large-sized, heterogeneous, and potentially low-quality data efficiently. The input of MapSDI is a set of data sources and mapping rules being generated by a mapping language such as RML. First, MapSDI pre-processes the sources based on semantic information extracted from mapping rules, by performing basic database operators; it projects out required attributes, eliminates duplicates, and selects relevant entries. All these operators are defined based on the knowledge encoded by the mapping rules which will be then used by the semantification engine (or RDFizer) to produce a knowledge graph. We have empirically studied the impact of MapSDI on existing RDFizers, and observed that knowledge graph creation time can be reduced on average in one order of magnitude. It is also shown, theoretically, that the sources and rules transformations provided by MapSDI are data-lossless.

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Integrating Relational Databases with the Semantic Web: A Reflection

Cited by 6 publications

References 49 publications

Ontology Based Access Control: A Case Study through Ontology Based Data Access

Ontology Based Access Control: A Case Study through Ontology Based Data Access

A Semi-Automatic Semantic Consistency-Checking Method for Learning Ontology from Relational Database

MapSDI: A Scaled-Up Semantic Data Integration Framework for Knowledge Graph Creation

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