The present study identifies and assesses the bibliographic trend in Artificial Intelligence (AI) research for the years 2015-2020 using the science mapping method of bibliometric study. The required data has been collected from the Scopus database. To make the collected data analysis-ready, essential data transformation was performed manually and with the help of a tool viz. OpenRefine. For determining the trend and performing the mapping techniques, top five open access and commercial journals of AI have been chosen based on their citescore driven ranking. The work includes 6880 articles published in the specified period for analysis. The trend is based on Country-wise publications, year-wise publications, topical terms in AI, top-cited articles, prominent authors, major institutions, involvement of industries in AI and Indian appearance. The results show that compared to open access journals; commercial journals have a higher citescore and number of articles published over the years. Additionally, IEEE is the prominent publisher which publishes 84% of the top-cited publications. Further, China and the United States are the major contributors to literature in the AI domain. The study reveals that neural networks and deep learning are the major topics included in top AI research publications. Recently, not only public institutions but also private bodies are investing their resources in AI research. The study also investigates the relative position of Indian researchers in terms of AI research. Present work helps in understanding the initial development, current stand and future direction of AI.

Ontological knowledge modelling of epics, though being an established research arena backed by concrete multilingual and multicultural works, still suffer from two key shortcomings. Firstly, all epic ontological models developed till date have been designed following ad-hoc methodologies, most often, combining existing general purpose ontology development methodologies. Secondly, none of the ad-hoc methodologies consider the potential reuse of existing epic ontological models for enrichment, if available. The paper presents, as a unified solution to the above shortcomings, the design and development of GENOME - the first dedicated methodology for iterative ontological modelling of epics, potentially extensible to works in different research arenas of digital humanities in general. GENOME is grounded in transdisciplinary foundations of canonical norms for epics, knowledge modelling best practices, application satisfiability norms and cognitive generative questions. It is also the first methodology (in epic modelling but also in general) to be flexible enough to integrate, in practice, the options of knowledge modelling via reuse or from scratch. The feasibility of GENOME is validated via a first brief implementation of ontological modelling of the Indian epic - Mahabharata by reusing an existing ontology. The preliminary results are promising, with the GENOME-produced model being both ontologically thorough and performance-wise competent