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Exploring the Contemporary Area of Ontology Research
Published in Archana Patel, Narayan C. Debnath, Bharat Bhushan, Semantic Web Technologies, 2023
The generic workflow discusses the process of specifying semantic metadata models (4b) as well. In order to reuse the prevailing semantic model, both contents of the ontology and the ontology need to be properly illustrated. Thus, describing ontologies will make the process simpler for the users to select and employ a specific ontology for their project. The metadata vocabularies like Metadata Vocabulary for Ontology Description and Publication [14] (MOD) can be taken advantage of for characterising and annotating the ontologies. It can likewise be exploited as an explicit OWL ontology by ontology repositories/ libraries to develop and offer a semantic interpretation of ontologies as linked data [15]. In order to have a straightforward understanding of ontology’s technology and ecosystem, ontology documentation plays an important role [16]. There are various ontology documentation tools available in literature like WIzard for DOCumenting Ontologies (WIDOCO) [17], OnToology [18], etc. which can process such documentation. WIDOCO guides stakeholders through the strides to be adopted when documenting ontology, relying on familiar best practices, and suggesting missing metadata that should be introduced. WIDOCO also facilitates customising the generated documentation, empowering stakeholders to pick which features they wish to incorporate in their documents17 (e.g., sections, diagrams, provenance information, etc.). Similarly, OnToology is a web-based device to automate a portion of the collaborative ontology development process. Provided a depository with an owl file, OnToology will examine it and design diagrams, complete documentation and verification based on familiar risks [18]. This FAIRification process emphasises the importance of ontologies and vocabularies in FAIR data principle as they are enablers for making the data into FAIR data.
Research data management of structural health monitoring projects and subsequent applications of artificial intelligence methods
Published in Joan-Ramon Casas, Dan M. Frangopol, Jose Turmo, Bridge Safety, Maintenance, Management, Life-Cycle, Resilience and Sustainability, 2022
P. Simon, R. Herrmann, R. Schneider, F. Hille, M. Baeßler, R. El-Athman
Especially data originating from publicly funded research projects should not be lost after its primary objective is completed but made accessible for further use. To enable this, the research community has formulated the FAIR (Findability, Accessibility, Interoperability, and Reuse) Guiding Principles for research data management (RDM) [1]. In this work, we highlight the characteristics of SHM data and metadata and propose a way of applying the FAIR data principles to manage this type of data.
Seven HCI Grand Challenges
Published in International Journal of Human–Computer Interaction, 2019
Constantine Stephanidis, Gavriel Salvendy, Margherita Antona, Jessie Y. C. Chen, Jianming Dong, Vincent G. Duffy, Xiaowen Fang, Cali Fidopiastis, Gino Fragomeni, Limin Paul Fu, Yinni Guo, Don Harris, Andri Ioannou, Kyeong-ah (Kate) Jeong, Shin’ichi Konomi, Heidi Krömker, Masaaki Kurosu, James R. Lewis, Aaron Marcus, Gabriele Meiselwitz, Abbas Moallem, Hirohiko Mori, Fiona Fui-Hoon Nah, Stavroula Ntoa, Pei-Luen Patrick Rau, Dylan Schmorrow, Keng Siau, Norbert Streitz, Wentao Wang, Sakae Yamamoto, Panayiotis Zaphiris, Jia Zhou
Big data goes hand in hand with IoT, both being recent technological evolutions that will definitely constitute core components of future technologically augmented environments. Big data, due to the harvesting of large sets of personal data coupled with the use of state of the art analytics, outlines additional threats to privacy, such as automated decision making (when decisions about an individual’s life are handed to automated processes), which raises concerns regarding discrimination, self-determination, and the narrowing of choices (Tene & Polonetsky, 2013). For example, predictive analytics may hinder implications for individuals prone to illness, crime, or other socially unacceptable characteristics or behaviors (Tene & Polonetsky, 2013). Other obscure possibilities are individuals (mistakenly) being denied opportunities based on the actions of others, the reinforcement of existing inequalities for vulnerable user groups (e.g. low-income consumers), as well as malevolent attempts and misleading offers to vulnerable individuals, such as seniors with Alzheimer or individuals with addictions (Federal Trade Commission, 2016). Responsible and fair data management and analysis is required from researchers to avoid inducing bias and discrimination (Stoyanovich, Abiteboul, & Miklau, 2016).