Graph Based Analytics and Review of Knowledge Graph Field by Using Two Decade Data- Finding Paradigms and Opportunities

Abstract

Knowledge Graph Embedding (KG Embed) has an important role in extracting the query and retrieving the required information in the technology specifically in scientific research. A surge is seen in the growth of knowledge graphs and also in knowledge representation and reasoning. This paper with the help of the knowledge graph creation helps in finding the hottest topic for research, Specifically, this study includes four research repositories that are Google Scholar (GS), IEEE Xplore, Science, and Worldwide Science (WWS). This study is based on keyword searching, which includes four keywords that are Knowledge graph (KG), Knowledge graph embedding (KG Embed), Recommendation system knowledge graph (RS KG), and Recommendation system in the education domain (RS in education). The data of several papers, articles, and books present in these four repositories from 2000 to 2022 is collected using these four keywords. After this, the paper visualizes this data using graphs and knowledge graphs with the help of parameters like centrality and clustering, which shows that Google Scholar contains more data than other repositories and the hottest topic in research is knowledge graph. It also gives an overview of how a knowledge graph is used in the recommendation system and how it is helpful. Based on the result that we got through the visualization of graphs and knowledge graphs; this study summarizes the suggestions and directions for researchers and practitioners to do future research on knowledge graphs by keeping all the related studies and points. This research takes into account the knowledge Graph research papers, articles, books, and theses from these four sources.