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Health-care ontologies and their terminologies play a vital role in knowledge representation and data integration for health information. In health-care systems, Internet of Technology (IoT) technologies provide data exchange among various entities and ontologies offer a formal description to present the knowledge of health-care domains. These ontologies are advised to assure the quality of their adoption and applicability in the real world.

Ontology assessment is an integral part of ontology construction and maintenance. It is always performed to identify inconsistencies and modeling errors by the experts during the ontology development. A smart health-care ontology (SHCO) has been designed to deal with health-care information and IoT devices. In this paper, an integrated approach has been proposed to assess the SHCO on different assessment tools such as Themis, Test-Driven Development (TDD)onto, Protégé and OOPs! Several test cases are framed to assess the ontology on these tools, in this research, Themis and TDDonto tools provide the verification for the test cases while Protégé and OOPs! provides validation of modeled knowledge in the ontology.

As of the best knowledge, no other study has been presented earlier to conduct the integrated assessment on different tools. All test cases are successfully analyzed on these tools and results are drawn and compared with other ontologies.

The developed ontology is analyzed on different verification and validation tools to assure the quality of ontologies.

This paper aims to curate open research knowledge graph (ORKG) with papers related to ontology learning and define an approach using ORKG as a computer-assisted tool to organize key-insights extracted from research papers.

Action research was used to explore, test and evaluate the use of the Open Research Knowledge Graph as a computer assistant tool for knowledge acquisition from scientific papers.

To extract, structure and describe research contributions, the granularity of information should be decided; to facilitate the comparison of scientific papers, one should design a common template that will be used to describe the state of the art of a domain.

This approach is currently used to document “food information engineering,” “tabular data to knowledge graph matching” and “question answering” research problems and the “neurosymbolic AI” domain. More than 200 papers are ingested in ORKG. From these papers, more than 800 contributions are documented and these contributions are used to build over 100 comparison tables. At the end of this work, we found that ORKG is a valuable tool that can reduce the working curve of state-of-the-art research.

On October 1, 2017, Gopal Vittal, Managing Director and Chief Executive Officer-India and South Asia, Bharti Airtel, was in his New Delhi office reviewing current trends and Airtel's position in Indian Telecom. His primary concern was the shifting data consumption trend in the Indian Telecom Industry (Exhibit 1) and the disruptive changes that were impacting pricing and profitability since the entry of Reliance Jio Infocomm Limited (Jio) in September 2016. Data consumption in Indian telecom had started increasing exponentially after the entry of Jio who offered lifetime free voice services followed by rock-bottom data tariffs. As Vittal reviewed the data, he wondered if the voice market through a non-VOIP provision was now saturated and would rapidly decline. He was also concerned about the price and revenue implications for Airtel. How might the voice market evolve? How should he act on the pricing front to enable Airtel revenues to continue to grow in the context of what appeared to be predatory pricing by Reliance Jio?

The purpose of this paper is to extend the theoretical understanding of the disclosure dilemma experienced by lesbian, gay and bisexual (LGB) employees. The study focuses on examining the factors related to self-disclosure such as co-worker support and fear of disclosure. Further, the study also aims at testing the impact of coworker support on the psychological well-being of LGB employees.

The study adopts a concurrent triangulation research design, which involves a simultaneous collection of quantitative and qualitative data. Using a survey questionnaire, data from 200 LGB employees were collected. Semi-structured interviews were carried out with 10 LGB employees. The qualitative findings were treated as complementary to the relationship tested through the quantitative method.

Quantitative results reveal that co-worker support positively impacts self-disclosure by LGB employees. And the fear of disclosure mediates the relationship between coworker support and self-disclosure. Further, self-disclosure is positively related to psychological well-being. The qualitative study brings insights from the lived experiences around these factors. This research expands knowledge about the factors associated with the disclosure of LGB employees.

The study examines the factors related to the disclosure of sexual identity and the experiences of the dilemma of LGB employees in the workplace. Most of the studies in the context of LGB have adopted a quantitative research design. The current study adopts a mixed methods approach with a concurrent triangulation research design.