Using Ontologies for Semantic Data Integration

Giacomo, Giuseppe De; Lembo, Domenico; Lenzerini, Maurizio; Poggi, Antonella; Rosati, Riccardo

doi:10.1007/978-3-319-61893-7_11

Cited by 67 publications

(33 citation statements)

References 32 publications

(42 reference statements)

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“…It allows for searching via multiple terms, including keywords, partial phrases, research area, and communities. Data integration is also a considerable challenge and De Giacomo and colleagues 30 survey approaches to this using ontology-based approaches. In general three components are used: (1) an ontology providing a high level representation of a domain, (2) existing data sources, and (3) a mapping between the two layers.…”

Section: State Of the Artmentioning

confidence: 99%

Tackling the Challenges of 21st-Century Open Science and Beyond: A Data Science Lab Approach

et al. 2020

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Section: State Of the Artmentioning

confidence: 99%

Tackling the Challenges of 21st-Century Open Science and Beyond: A Data Science Lab Approach

et al. 2020

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“…In software engineering, ontologies have a wide range of applications, including model transformations, cloud security engineering, decision support, search, and semantic integration (Kappel et al, 2006;Aljawarneh, Alawneh & Jaradat, 2017;Maurice et al, 2017;Bartussek et al, 2018;De Giacomo et al, 2018). Semantic integration is the process of merging the semantic contents of multiple ontologies.…”

Section: Ontology and Software Engineeringmentioning

confidence: 99%

Reverse engineering approach for improving the quality of mobile applications

Elsayed

ElDahshan

El-Sharawy

et al. 2019

PeerJ Computer Science

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BackgroundPortable-devices applications (Android applications) are becoming complex software systems that must be developed quickly and continuously evolved to fit new user requirements and execution contexts. Applications must be produced rapidly and advance persistently in order to fit new client requirements and execution settings. However, catering to these imperatives may bring about poor outline decisions on design choices, known as anti-patterns, which may possibly corrupt programming quality and execution. Thus, the automatic detection of anti-patterns is a vital process that facilitates both maintenance and evolution tasks. Additionally, it guides developers to refactor their applications and consequently enhance their quality.MethodsWe proposed a general method to detect mobile applications’ anti-patterns that can detect both semantic and structural design anti-patterns. The proposed method is via reverse-engineering and ontology by using a UML modeling environment, an OWL ontology-based platform and ontology-driven conceptual modeling. We present and test a new method that generates the OWL ontology of mobile applications and analyzes the relationships among object-oriented anti-patterns and offer methods to resolve the anti-patterns by detecting and treating 15 different design’s semantic and structural anti-patterns that occurred in analyzing of 29 mobile applications. We choose 29 mobile applications randomly. Selecting a browser is not a criterion in this method because the proposed method is applied on a design level. We demonstrate a semantic integration method to reduce the incidence of anti-patterns using the ontology merging on mobile applications.ResultsThe proposed method detected 15 semantic and structural design anti-patterns which have appeared 1,262 times in a random sample of 29 mobile applications. The proposed method introduced a new classification of the anti-patterns divided into four groups. “The anti-patterns in the class group” is the most group that has the maximum occurrences of anti-patterns and “The anti-patterns in the operation group” is the smallest one that has the minimum occurrences of the anti-patterns which are detected by the proposed method. The results also showed the correlation between the selected tools which we used as Modelio, the Protégé platform, and the OLED editor of the OntoUML. The results showed that there was a high positive relation between Modelio and Protégé which implies that the combination between both increases the accuracy level of the detection of anti-patterns. In the evaluation and analyzing the suitable integration method, we applied the different methods on homogeneous mobile applications and found that using ontology increased the detection percentage approximately by 11.3% in addition to guaranteed consistency.

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“…This would lead to a strong relationship between software design and ontology development. In software engineering, ontologies have a wide range of applications, including model transformations, cloud security engineering, decision support, search and semantic integration (Kappel et al, 2006;Aljawarneh et al, 2017;Maurice et al, 2017;Bartussek et al, 2018;De Giacomo et al 2018). Semantic integration is the process of merging the semantic contents of multiple ontologies.…”

Section: Ontology and Software Engineeringmentioning

confidence: 99%

Peer Review #2 of "Reverse engineering approach for improving the quality of mobile applications (v0.1)"

Dahlke¹

2019

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Background: Portable-devices applications (Android applications) are becoming complex software systems that must be developed quickly and evolve continuously to fit new user requirements and execution contexts. Applications must be produced rapidly and advance persistently in order to fit new client requirements and execution settings. However, catering to these imperatives may bring about poor outline decisions on design choices, known as anti-patterns, which may possibly corrupt programming quality and execution. Thus, the automatic detection of anti-patterns is a vital process that facilitates both maintenance and evolution tasks. Additionally, it guides developers to refactor their applications and consequently enhance their quality. Methods: We proposed a general method to detect mobile applications' anti-patterns that can detect both semantic and structural design anti-patterns. The proposed method is via reverse-engineering and Ontology by using UML modeling environment, OWL ontology-based platform and Ontology-driven conceptual modeling. We present and test a new method that generates OWL Ontology of mobile applications and analyzes the relationships among object-oriented anti-patterns and offer methods to resolve the anti-patterns by detecting and treating 15 different design's semantic and structural antipatterns that occurred in analyzing of 29 mobile applications. We choose 29 mobile applications randomly. Selecting a browser is not a criterion in this method, because the proposed method is applied on a design level. We demonstrate a semantic integration method to reduce the incidence of antipatterns using the Ontology merging on mobile applications. Results: The proposed method detected 15 semantic and structural design anti-patterns which have appeared 1262 times in a random sample of 29 mobile applications. The proposed method introduced a new classification of the anti-patterns divided into four groups. ''The anti-patterns in the class group'' is the most group that has the maximum occurrences of anti-patterns and ''The anti-patterns in the operation group'' is the smallest one that has the minimum occurrences of the anti-patterns which are detected by the proposed method. The results also showed the correlation between the selected tools which we used as Modelio, Protégé platform and OLED editor of the OntoUML. The results showed that there was a high positive relation between Modelio and Protégé which implies that the combination between both increases the accuracy level of the detection of anti-patterns. In the evaluation and analyzing the suitable integration method, we applied the different methods on homogeneous mobile applications and found that using Ontology increased the detection percentage approximately by 11.3% in addition to guaranteed consistency.

show abstract

Using Ontologies for Semantic Data Integration

Cited by 67 publications

References 32 publications

Tackling the Challenges of 21st-Century Open Science and Beyond: A Data Science Lab Approach

Tackling the Challenges of 21st-Century Open Science and Beyond: A Data Science Lab Approach

Reverse engineering approach for improving the quality of mobile applications

Peer Review #2 of "Reverse engineering approach for improving the quality of mobile applications (v0.1)"

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