Ontology-driven advanced drug-drug interaction

Naz, Tabbasum; Akhtar, Muhammad; Shahzad, Syed Khuram; Fasli, Maria; Iqbal, Muhammad Waseem; Naqvi, Muhammad Raza

doi:10.1016/j.compeleceng.2020.106695

Cited by 16 publications

(9 citation statements)

References 5 publications

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“…Reasoner detects and finds inconsistencies or contradictions of ontologies structure and data based on mathematical models [27]. It provides logical deductions based on inference rules defined and specified in description logic and uses forward chaining or backward chaining to perform inference [28]. Pellet Reasoner is used to validate our ontology model.…”

Section: Validationmentioning

confidence: 99%

Ontology Driven Testing Strategies for IoT Applications

Naqvi¹,

Iqbal²,

Ashraf³

et al. 2022

Computers, Materials &Amp; Continua

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Internet-of-Things (IoT) has attained a major share in embedded software development. The new era of specialized intelligent systems requires adaptation of customized software engineering approaches. Currently, software engineering has merged the development phases with the technologies provided by industrial automation. The improvements are still required in testing phase for the software developed to IoT solutions. This research aims to assist in developing the testing strategies for IoT applications, therein ontology has been adopted as a knowledge representation technique to different software engineering processes. The proposed ontological model renders 101 methodology by using Protégé. After completion, the ontology was evaluated in three-dimensional view by the domain experts of software testing, IoT and ontology engineering. Satisfied results of the research are showed in interest of the specialists regarding proposed ontology development and suggestions for improvements. The Proposed reasoning-based ontological model for development of testing strategies in IoT application contributes to increase the general understanding of tests in addition to assisting for the development of testing strategies for different IoT devices.

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Section: Validationmentioning

confidence: 99%

Ontology Driven Testing Strategies for IoT Applications

Naqvi¹,

Iqbal²,

Ashraf³

et al. 2022

Computers, Materials &Amp; Continua

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“…The SPARQL querying is used to verify the storage and retrieval of interface knowledge associated with the context [27][28][29]. The context is passed to knowledge-base as querying argument and respective interface details are inquired.…”

Section: Experimentation Using Sparql Queriesmentioning

confidence: 99%

Mobile Devices Interface Adaptivity Using Ontologies

Iqbal¹,

Naqvi²,

Khan³

et al. 2022

Computers, Materials &Amp; Continua

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Currently, many mobile devices provide various interaction styles and modes which create complexity in the usage of interfaces. The context offers the information base for the development of Adaptive user interface (AUI) frameworks to overcome the heterogeneity. For this purpose, the ontological modeling has been made for specific context and environment. This type of philosophy states to the relationship among elements (e.g., classes, relations, or capacities etc.) with understandable satisfied representation. The context mechanisms can be examined and understood by any machine or computational framework with these formal definitions expressed in Web ontology language (WOL)/Resource description frame work (RDF). The Protégé is used to create taxonomy in which system is framed based on four contexts such as user, device, task and environment. Some competency questions and use-cases are utilized for knowledge obtaining while the information is refined through the instances of concerned parts of context tree. The consistency of the model has been verified through the reasoning software while SPARQL querying ensured the data availability in the models for defined use-cases. The semantic context model is focused to bring in the usage of adaptive environment. This exploration has finished up with a versatile, scalable and semantically verified context learning system. This model can be mapped to individual User interface (UI) display through smart calculations for versatile UIs.

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“…Reasoner detects and finds inconsistencies or contradictions of ontologies structure and data based on mathematical models [56]. It provides logical deductions based on inference rules defined and specified in description logic and uses forward chaining or backward chaining to perform inference [57]. Pellet Reasoner is used to validate our ontology model.…”

Section: ) Verification Through Reasonermentioning

confidence: 99%

User Context Ontology for Adaptive Mobile-Phone Interfaces

Iqbal

Nickabadi²,

Shahzad

et al. 2021

IEEE Access

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The Adaptive User Interface (AUI) adapts to the changes in the context of use and provides improved interaction abilities for different users. The adaptivity in the user interfaces requires in depth knowledge of context. There is a need to enrich user profiles to achieve the personalized services with the ability to adapt the user's context. The context can be reflected in a particular kind of knowledge and hence modeled as ontology. Ontology based context models are effective means to handle complex situations that support the sharing or integration of context information. This paper presents ontology based context model using OWL for adaptive mobile devices. It models the context over its four major elements including device, user, environment (location and time) and activity. The proposed ontology was derived in different classes, relationships, associations, dependencies and constraints to model dynamic context. Ontologies present a standardized, consistent and shareable context model. The context model and consequent context snapshots can be acknowledged by AUI to present a suitable user interface. The ontology was developed using Protégé on the basis of each context type having different values. Semantic querying (SPARQL) was used for knowledge acquisition. Moreover, the Pellet and HermiT Reasoner were used to verify the rules, relations and constraints to avoid the inconsistency between classes. Comparative to other context models for adaptive interfaces, ontological model provides more of scalability and growth with learning new context in to the shared context knowledge.

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Ontology-driven advanced drug-drug interaction

Cited by 16 publications

References 5 publications

Ontology Driven Testing Strategies for IoT Applications

Ontology Driven Testing Strategies for IoT Applications

Mobile Devices Interface Adaptivity Using Ontologies

User Context Ontology for Adaptive Mobile-Phone Interfaces

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