Developing ontology-driven conceptual data models

El-Ghalayini, Haya; Odeh, Mohammed; McClatchey, Richard

doi:10.1145/1874590.1874594

Cited by 3 publications

(7 citation statements)

References 8 publications

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“…Our proposed OWL2ToRDB transformation [29,30] supports this common semantics [9]. It maps ontology classes to relational tables; individuals -to rows; functional object properties (or having cardinality restricted to 1) -to foreign keys; object properties with cardinality greater than 1 -to tables having foreign key relations from tables representing classes of property domain and range; SubClassOf relation is mapped to a foreign key from a parent table to a child table, for which that foreign key, a primary key of a parent table, is also a primary key; data properties are mapped to columns; OWL data typesto RDB data types.…”

Section: Common Semantics Of Ontologies and Information Systemsmentioning

confidence: 99%

“…Semantics of conceptual models should involve (in UML terms) object types, relationships, properties, instances and constraints of a problem domain. This semantics is equivalent to semantics of ontology and can be defined on the base of ontological analysis provided by El-Ghalayini et al [9] where authors state that ontologies and conceptual models have much in common. Ontology as well as a conceptual model consists of concepts, properties, individuals, and constraints (restrictions and axioms).…”

Section: Common Semantics Of Ontologies and Information Systemsmentioning

confidence: 99%

“…In contrast with conceptual models, properties are considered first-class elements in ontology and can exist without specifying classes related by that property. Such properties are senseless in databases and conceptual models [9]. El-Ghalayini et al propose to map only properties that are related to classes satisfying constraints in the ontology under consideration.…”

Section: Common Semantics Of Ontologies and Information Systemsmentioning

confidence: 99%

“…Though storing Web Ontology Language OWL 2 ontology [3,4] in one vertical table [5] or ontology metamodel based schema [6,7] guarantee preservation of ontological semantics, obtained relational schemas are suitable for processing ontologies only as they are meaningless from the conceptual point of view; they do not avail advantages of relational databases, and are not applicable for applications of information systems. There are arguments for having different storing models [8,9] reflecting common semantics of ontologies and conceptual models familiar for domain experts and application developers. We have proposed a reversible OWL2ToRDB transformation pursuing a hybrid approach according which part of ontology concepts is directly mapped to relational schema on the base of their common semantics; ontology constructs having no direct equivalents in database schema are stored in metadata tables [1].…”

Section: Introductionmentioning

confidence: 99%

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Preserving Semantics of Owl 2 Ontologies in Relational Databases Using Hybrid Approach

Vyšniauskas

Nemuraite

Paradauskas

2012

ITC

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The goal of the paper is to define requirements to OWL 2 ontologies, under which their semantics may be preserved in a relational database, and to demonstrate that the hybrid approach for transforming OWL 2 ontologies into relational databases possesses such capability. The hybrid approach maps part of ontology concepts to relational database concepts on the base of their common semantics; ontology constructs having no direct equivalents in databases are stored in metatables. The paper defines requirements for ontologies under transformation as ontology normalization and integrity rules, and presents a set of SQL queries for extracting rich data, covering semantics of source ontology, from the resulting database. The capability of the hybrid approach to preserve semantics of OWL 2 ontologies in relational databases is demonstrated with a representative example of a Vehicle ontology.

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Section: Common Semantics Of Ontologies and Information Systemsmentioning

confidence: 99%

Section: Common Semantics Of Ontologies and Information Systemsmentioning

confidence: 99%

Section: Common Semantics Of Ontologies and Information Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Preserving Semantics of Owl 2 Ontologies in Relational Databases Using Hybrid Approach

Vyšniauskas

Nemuraite

Paradauskas

2012

ITC

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“…According to [10] for each class from ontology must be defined a named entity super-class type in data model, and each ontology subclass is presented with entity subtype, with restriction that subtypes in data model must be different objects: …”

Section: Reasoning Rulesmentioning

confidence: 99%

Ontology-based reasoning for entity-relationship data model semantic evaluation

et al. 2017

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Original scientific paper Conceptual modeling is one of the most important activities in the modeling phase of information systems development most commonly presented by entity-relationship data model. This paper presents a system for entity-relationship data model semantic evaluation that is based on comparing ontology with data model elements. This approach is based on domain ontology and data model formalization at predicate calculus form that is suitable for reasoning. A set of reasoning rules for ontology to data model mapping was defined. The whole process is empirically verified and confirmed. For this purpose it has been developed a software tool for ontology and data model transformation to predicate logic form and then to a set of Prolog-like clauses. After integration of these sets of clauses and rules, a Prolog-system was used for reasoning in order to quantitatively express the quality of data model with appropriate metric. Keywords: data model; ontology; reasoning rules; semantic evaluation Ontološki zasnovano rasuđivanje za semantičku evaluaciju modela entiteta-poveznikaIzvorni znanstveni članak Konceptualno modeliranje je jedna od najbitnijih aktivnosti u fazi modeliranja informacionog sustava najčešće prezentirana modelom entiteta-poveznika. Ovaj rad predstavlja sustav za semantičko evaluiranje modela entiteta-poveznika koji se bazira na komparaciji elemenata ontologije i modela podataka. Ovaj pristup je baziran na formalizaciji domenske ontologije i modela podataka u formu predikatskog računa koji je pogodan za rasuđivanje. Definiran je skup pravila rasuđivanja za mapiranje ontologije u model podataka. Ceo postupak je empirijski verificiran i potvrđen. Za ovu svrhu je razvijen softverski alat koji ontologiju i model podataka transformira u predikatski oblik i zatim u skup Prologolikih klauzula. Nakon integriranja ovih klauzula i pravila uporabljen je Prolog sustav za rasuđivanje sa ciljem da se kvantitativno izrazi kvalitet modela podataka sa odgovarajućom metrikom.

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Data management for developing digital twin ontology model

Singh

Shehab

Higgins

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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Digital Twin (DT) is the imitation of the real world product, process or system. Digital Twin is the ideal solution for data-driven optimisations in different phases of the product lifecycle. With the rapid growth in DT research, data management for digital twin is a challenging field for both industries and academia. The challenges for DT data management are analysed in this article are data variety, big data & data mining and DT dynamics. The current research proposes a novel concept of DT ontology model and methodology to address these data management challenges. The DT ontology model captures and models the conceptual knowledge of the DT domain. Using the proposed methodology, such domain knowledge is transformed into a minimum data model structure to map, query and manage databases for DT applications. The proposed research is further validated using a case study based on Condition-Based Monitoring (CBM) DT application. The query formulation around minimum data model structure further shows the effectiveness of the current approach by returning accurate results, along with maintaining semantics and conceptual relationships along DT lifecycle. The method not only provides flexibility to retain knowledge along DT lifecycle but also helps users and developers to design, maintain and query databases effectively for DT applications and systems of different scale and complexities.

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Developing ontology-driven conceptual data models

Cited by 3 publications

References 8 publications

Preserving Semantics of Owl 2 Ontologies in Relational Databases Using Hybrid Approach

Preserving Semantics of Owl 2 Ontologies in Relational Databases Using Hybrid Approach

Ontology-based reasoning for entity-relationship data model semantic evaluation

Data management for developing digital twin ontology model

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