Ontologies in human–computer interaction: A systematic literature review

Abstract

Human–Computer Interaction (HCI) is a multidisciplinary area that involves a diverse body of knowledge and a complex landscape of concepts, which can lead to semantic problems, hampering communication and knowledge transfer. Ontologies have been successfully used to solve semantics and knowledge-related problems in several domains. This paper presents a systematic literature review that investigated the use of ontologies in the HCI domain. The main goal was to find out how HCI ontologies have been used and developed. 35 ontologies were identified. As a result, we noticed that they cover different HCI aspects, such as user interface, interaction phenomenon, pervasive computing, user modeling / profile, HCI design, interaction experience and adaptive interactive system. Although there are overlaps, we did not identify reuse among the 35 analyzed ontologies. The ontologies have been used mainly to support knowledge representation and reasoning. Although ontologies have been used in HCI for more than 25 years, their use became more frequent in the last decade, when ontologies address a higher number of HCI aspects and are represented as both conceptual and computational models. Concerning how ontologies have been developed, we noticed that some good practices of ontology engineering have not been followed. Considering that the quality of an ontology directly influences the quality of the solution built based on it, we believe that there is an opportunity for HCI and ontology engineering professionals to get closer to build better and more effective ontologies, as well as ontology-based solutions.

Big Data Management Algorithms, Deep Learning-Based Object Detection Technologies, and Geospatial Simulation and Sensor Fusion Tools in the Internet of Robotic Things

“…Intelligent manufacturing environments and cyber-physical production systems [81][82][83][84] develop on decision-making process automation [85][86][87][88], digital twin and cloud computing technologies, and smart infrastructure sensors. Machine and reinforcement learning algorithms and distributed wireless sensor networks [89][90][91][92] articulate autonomous routing decisions and enhance big data scalability. Swarm robotic behaviors integrate cognitive robotics, deep and machine learning algorithms, and data communication networks.…”

“…[ [85][86][87][88] IoRT devices accurately process and analyze collected data by deploying image recognition technology, geospatial simulation and sensor fusion tools, and intelligent optimization algorithms. [89][90][91][92] Robot-based assistance of IoT-enabled edge computing technologies requires blockchain-enabled edge computing systems, heterogeneous computational collective intelligence and processes, and distributed edge devices and algorithms.…”

“…Product simulation models, data mining and spatio-temporal fusion algorithms, remote sensing technologies, and decision support tools optimize robotized manufacturing systems. IoRT devices accurately process and analyze collected data [89][90][91][92] by deploying image recognition technology, geospatial simulation and sensor fusion tools, and intelligent optimization algorithms. By collecting and processing heterogeneous networked industrial data, IoRT devices and cyber-physical systems assist in knowledge representation and transfer and in collaborative optimization of production performance throughout pervasive computing environments.…”

“…Our specific contribution is to clarify how mobile connected IoRT devices [73][74][75][76], cloud and pattern recognition technologies, networked robotics, and automated machines [77][78][79][80] configure autonomous manufacturing units in dynamic simulation environments in terms of robotic behavior control, sensor and actuator interconnections, and real-time data processing and analysis [81][82][83][84]. Correlations with previously published literature comprise analyses on how industrial cloud robotics integrates autonomous production systems, smart manufacturing task management [85][86][87][88], simulation optimization algorithms, and data visualization tools, while disparities encompass our integration of the sources on how autonomous robotized devices develop on virtual simulation tools, real-time monitoring capabilities [89][90][91][92], data fusion techniques, and cloud computing algorithms. The research problems are whether computer vision techniques, geospatial data mining [93][94][95][96], simulation-based digital twins, and real-time monitoring technology [97][98][99][100] optimize remote sensing robots [101][102][103][104][105].…”

Using Networked Ontologies to Support the Development of Software Systems with Adaptive User Interface

“…In the literature, some works have explored the use of ontologies to develop AUI systems (Costa et al, 2021) (e.g., (Bonacin et al, 2022), (Braham et al, 2021)), (Fedasyuk and Lutsyk, 2021), (Stefanidi et al, 2022), (Sala et al, 2021) and (Khan and Khusro, 2019). However, the ontologies often are very specific, i.e., they can only be used to solve a particular problem in the context of the system to which they were created, and are used mainly at the operational level.…”