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Ontology of a domain mainly consists of a set of concepts and their semantic relations. It is typically constructed and maintained by using ontology editors with substantial human intervention. It is desirable to perform the task automatically, which has led to the development of ontology learning techniques. One of the main challenges of ontology learning from the text is to identify key concepts from the documents. A wide range of techniques for key concept extraction have been proposed but are having the limitations of low accuracy, poor performance, not so flexible and applicability to a specific domain. The propose of this study is to explore a new method to extract key concepts and to apply them to literature in the nuclear domain.

In this article, a novel method for key concept extraction is proposed and applied to the documents from the nuclear domain. A hybrid approach was used, which includes a combination of domain, syntactic name entity knowledge and statistical based methods. The performance of the developed method has been evaluated from the data obtained using two out of three voting logic from three domain experts by using 120 documents retrieved from SCOPUS database.

The work reported pertains to extracting concepts from the set of selected documents and aids the search for documents relating to given concepts. The results of a case study indicated that the method developed has demonstrated better metrics than Text2Onto and CFinder. The method described has the capability of extracting valid key concepts from a set of candidates with long phrases.

The present study is restricted to literature coming out in the English language and applied to the documents from nuclear domain. It has the potential to extend to other domains also.

The work carried out in the current study has the potential of leading to updating International Nuclear Information System thesaurus for ontology in the nuclear domain. This can lead to efficient search methods.

This work is the first attempt to automatically extract key concepts from the nuclear documents. The proposed approach will address and fix the most of the problems that are existed in the current methods and thereby increase the performance.

The purpose of this paper is to investigate the scholarly contribution of literature published on the much-hyped term fake news and associated terms such as misinformation, disinformation and post-truth in various disciplines, which contributes heavily to information disorder.

The study conducted a bibliometric inquiry of literature published in Scopus and Web of Science (WoS) databases (2001-2020) and steered in-depth quantitative content analysis of top-cited publications. The data mining covers 1,776 and 1,056 publications from WoS and Scopus databases, respectively. Bibliometrix R-package, VOSviewer Software tool and Microsoft Excel were used for analysis.

The study concluded that the past seven years (2014–2020) are the most productive period in studying fake news and its associated terms due to the unprecedented rise of social media and digital media. The prominent themes of the study were conducted in political, health, technology, media and social media space, whereas the output is minor in the pure science field. It is also inferred that both databases are contributing consistently in the domain of fake news literature.

The study helps in expansion of knowledge based on the research topic as well as in understanding the evolution of fake news in support of further research in this area.

Mapping scholarly contributions of scientific research provides a guiding approach and helps counter the information chaos stimulated by fake news phenomena in the digital era.