02651nas a2200229   4500000000100000000000100001008004100002260001200043653001800055653002700073653002500100653001500125100001800140700002000158700002300178245008600201856008000287300001200367490000600379520202200385022001402407       2023                            d  c06/202310aOverlap Index10aRecommendation Systems10aKnowledge Management10aOntologies1 aGerard Deepak1 aAdithya Vibakar1 aA. Santhanavijayan00aOntoInfoG++: A Knowledge Fusion Semantic Approach for Infographics Recommendation  uhttps://www.ijimai.org/journal/sites/default/files/2023-05/ijimai8_2_20.pdf  a213-2230 v83 aAs humans tend to improvise and learn on a constant basis, the need for visualizing and recommending knowledge is increasing. Since the World Wide Web is exploded with a lot of multimedia content and with a growing amount of research papers on the Web, there is a potential need for inferential multimedia like the infographics which can lead to an ultimate new level of learning from most viable information sources on the Web. The potential growth and future of technology have called for the need of a Web 3.0 compliant infographic recommendation system in order to be able to visualize, design and develop aesthetically. The trend of the Web has asked for better infographic recommendations in the attempt of technological exploration. This paper proposes the OntoInfoG++ which is a knowledge centric recommendation approach for Infographics that encompasses the amalgamation of metadata derived from multiple heterogeneous sources and the crowd sourced ontologies to recommend infographics based on the topic of interest of the user. The user- clicks are taken into consideration along with an Ontology which is modeled using the titles and the keywords extracted from the dataset comprising of research papers. The approach models user topic of interest from the Query Words, Current User-Clicks, and from standard Knowledge Stores like the BibSonomy, DBpedia, Wikidata, LOD Cloud, and crowd sourced Ontologies. The semantic alignment is achieved using three distinct measures namely the Horn’s index, EnAPMI measure and information entropy. The resultant infographic recommendation has been achieved by computing the semantic similarity between enriched topics of interest and infographic labels and arrange the recommended infographics in the increasing order of their semantic similarity to yield a chronological order for the meaningful arrangement of infographics. The OntoInfoG++ has achieved an overall F-measure of 97.27 % which is the best-in-class F-measure for an infographic recommendation system.  a1989-1660