User interface design patterns and ontology models for adaptive mobile applications

Braham, Amani; Buendía, Félix; Khemaja, Maha; Gargouri, Faïez

doi:10.1007/s00779-020-01481-5

Cited by 14 publications

(9 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By adding user goals to our ontology, we captured crucial tasks, such as attending a course, obtaining a gym subscription, making an online purchase, learning new skills, etc. [ 67 , 70 , 78 ].…”

Section: An Accessibility Ontology For Service Selectionmentioning

confidence: 99%

See 1 more Smart Citation

A Two-Phase Machine Learning Framework for Context-Aware Service Selection to Empower People with Disabilities

Namoun

Sen

Tufail

et al. 2022

Sensors

View full text Add to dashboard Cite

The use of software and IoT services is increasing significantly among people with special needs, who constitute 15% of the world’s population. However, selecting appropriate services to create a composite assistive service based on the evolving needs and context of disabled user groups remains a challenging research endeavor. Our research applies a scenario-based design technique to contribute (1) an inclusive disability ontology for assistive service selection, (2) semi-synthetic generated disability service datasets, and (3) a machine learning (ML) framework to choose services adaptively to suit the dynamic requirements of people with special needs. The ML-based selection framework is applied in two complementary phases. In the first phase, all available atomic tasks are assessed to determine their appropriateness to the user goal and profiles, whereas in the subsequent phase, the list of service providers is narrowed by matching their quality-of-service factors against the context and characteristics of the disabled person. Our methodology is centered around a myriad of user characteristics, including their disability profile, preferences, environment, and available IT resources. To this end, we extended the widely used QWS V2.0 and WS-DREAM web services datasets with a fusion of selected accessibility features. To ascertain the validity of our approach, we compared its performance against common multi-criteria decision making (MCDM) models, namely AHP, SAW, PROMETHEE, and TOPSIS. The findings demonstrate superior service selection accuracy in contrast to the other methods while ensuring accessibility requirements are satisfied.

show abstract

Section: An Accessibility Ontology For Service Selectionmentioning

confidence: 99%

“…Our proposed services branch combines several useful services that can help the disabled person perform daily routine tasks. It includes education, leisure, shopping, transportation, financial support, sports, social, medical, restaurant, accommodation, and reservation [ 23 , 70 , 83 , 84 ].…”

Section: An Accessibility Ontology For Service Selectionmentioning

confidence: 99%

A Two-Phase Machine Learning Framework for Context-Aware Service Selection to Empower People with Disabilities

Namoun

Sen

Tufail

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…It is also needed to have the reflection of context model to respective adaptive interface through their semantic relations and computable mapping functions. In the larger perspective, this research is part of User Centered Design (UCD) approach for adaptive user interface design and development [24]. The major goal of UCD is to offer optimized, efficient and user friendly products which increase the usability and satisfaction of users [4] [25] [26].…”

Section: Problem Statementmentioning

confidence: 99%

User Context Ontology for Adaptive Mobile-Phone Interfaces

Iqbal

Nickabadi²,

Shahzad

et al. 2021

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

“…The main target of the semantic module is to perform an automated reasoning over the MIDEP ontology [24,25] and to select the most relevant design patterns based on an ontology-based technique. In the following, we present an overview of the MIDEP ontology and describe the workflow of the semantic module.…”

Section: Semantic Modulementioning

confidence: 99%

A Hybrid Recommender System for HCI Design Pattern Recommendations

et al. 2021

Self Cite

View full text Add to dashboard Cite

User interface design patterns are acknowledged as a standard solution to recurring design problems. The heterogeneity of existing design patterns makes the selection of relevant ones difficult. To tackle these concerns, the current work contributes in a twofold manner. The first contribution is the development of a recommender system for selecting the most relevant design patterns in the Human Computer Interaction (HCI) domain. This system introduces a hybrid approach that combines text-based and ontology-based techniques and is aimed at using semantic similarity along with ontology models to retrieve appropriate HCI design patterns. The second contribution addresses the validation of the proposed recommender system regarding the acceptance intention towards our system by assessing the perceived experience and the perceived accuracy. To this purpose, we conducted a user-centric evaluation experiment wherein participants were invited to fill pre-study and post-test questionnaires. The findings of the evaluation study revealed that the perceived experience of the proposed system’s quality and the accuracy of the recommended design patterns were assessed positively.

show abstract

User interface design patterns and ontology models for adaptive mobile applications

Cited by 14 publications

References 18 publications

A Two-Phase Machine Learning Framework for Context-Aware Service Selection to Empower People with Disabilities

A Two-Phase Machine Learning Framework for Context-Aware Service Selection to Empower People with Disabilities

User Context Ontology for Adaptive Mobile-Phone Interfaces

A Hybrid Recommender System for HCI Design Pattern Recommendations

Contact Info

Product

Resources

About