Rules and Implementation for Generating Ontology from Relational Database

Ren, Yaoyao; Jiang, Lihong; Bu, Fenglin; Cai, Hongming

doi:10.1109/cgc.2012.48

“…OGSRD [3] is a method for ontology building from relational databases that uses construction rules of ontology elements (concepts, properties, axioms, individuals), but it ignores sets of tables that express association data. Ren et al [15] propose a rule-based transformation solution, which considers both schema transformation (relations, attributes, and constraints) and data extraction. DB2OWL [16] maps tables to classes and columns to predicates, using relational database schema characteristics' to assert sub-classes and object properties.…”

Section: Related Workmentioning

confidence: 99%

“…The works from [9,10,11] provided a classification covering aspects such as the existence of an initial ontology, its application in reverse engineering, automation level, and others. Notably, the approaches [12,13,3,14,15,16,6,17,18,19,5,20] aim to convert relational data models to ontologies, by providing mapping rules that translate the elements of a database schema into concepts and relationships of an ontology. The W3C RDB2RDF Working Group provides as well a survey [21] of different approaches of mapping relational databases to ontologies, including the definition of a reference architecture to compare them.…”

Section: Introductionmentioning

confidence: 99%

Exploring logical and hierarchical information to map relational databases into ontologies

Tissot

¹

,

Huve

²

,

Peres

³

et al. 2019

IJMSO

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Ontologies are formal specifications of conceptualizations. Their designs require to understand the concepts involved in the domain to be mapped. One well-known method to produce ontologies is to extract their concepts from relational databases. We conducted a practical study over a real-world scenario on applying existing rules and we identified open issues to be addressed, such as the utilization of logical metadata as a proper vocabulary, the implementation targeted to specific domains and mappings of hierarchical and self-hierarchical structures. In this paper, we present a novel approach that overcomes these issues. Our solution uses physical and logical models to enrich the terminology produced in the target ontology. It also contains a more comprehensive set of rules, taking into account instances and (self)hierarchies. We validate our approach with 2 experiments from the healthcare domain as input.

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“…Instead, we use the logical nomenclature in order to provide a proper vocabulary. Figure 7 Sample of ontology elements resulting by applying [15].…”

Section: Experiments 2: Comparing With Other Solutionsmentioning

confidence: 99%

“…Figures 6 and 7 contrast the resulting ontology based on our proposed set of rules against the ontology produced by [15]. Figure 6(a) shows some of resulting classes following our approach.…”

Section: Experiments 2: Comparing With Other Solutionsmentioning

confidence: 99%

Exploring logical and hierarchical information to map relational databases into ontologies

Tissot

¹

,

Huve

²

,

Peres

³

et al. 2019

IJMSO

View full text Add to dashboard Cite

Ontologies are formal specifications of conceptualizations. Their designs require to understand the concepts involved in the domain to be mapped. One well-known method to produce ontologies is to extract their concepts from relational databases. We conducted a practical study over a real-world scenario on applying existing rules and we identified open issues to be addressed, such as the utilization of logical metadata as a proper vocabulary, the implementation targeted to specific domains and mappings of hierarchical and self-hierarchical structures. In this paper, we present a novel approach that overcomes these issues. Our solution uses physical and logical models to enrich the terminology produced in the target ontology. It also contains a more comprehensive set of rules, taking into account instances and (self)hierarchies. We validate our approach with 2 experiments from the healthcare domain as input.

show abstract

“…[7]  Ontology follows java object oriented approach, such as inheritance to avoid redundancy that prevails in the existing systems.  It uses Data from API calls to create classes, properties and named individual.…”

Section: Ontology Templatementioning

confidence: 99%

Ontology based Chatbot (For E-commerce Website)

Vegesna¹,

Jain²,

Porwal³

2018

IJCA

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A working model of Ontology based chatbot is proposed that handles queries from users for an E-commerce website. It is mainly concerned with providing user the total control over the search result on the website. This chatbot helps the user by mapping relationships of the various entities required by the user, thus providing detailed and accurate information there by overcoming the drawbacks of traditional chatbots. The Ontology template is developed using Protégé which stores the knowledge acquired from the website APIs while the dialog manager is handled using Wit.ai. The integration of the dialog manager and the ontology template is managed through Python. The related response to the query will be formatted and returned to the user on the dialog manager.

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Rules and Implementation for Generating Ontology from Relational Database

Cited by 7 publications

References 7 publications

Exploring logical and hierarchical information to map relational databases into ontologies

Exploring logical and hierarchical information to map relational databases into ontologies

Exploring logical and hierarchical information to map relational databases into ontologies

Ontology based Chatbot (For E-commerce Website)

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