Scientific Knowledge Graph Creation and Analysis

Reviewing the internet for research often tends to be a time-intensive process. With a rapid increase in data available on the internet, one’s research can easily be swayed as there is a high probability of less important papers being displayed as search results. A significant percentage of research papers published lack credibility thus leading to an overall decline in the standard of research. With the objective of making the methodology of research straightforward and providing academia with essential and accurate information, we propose a system with the application of Knowledge Graphs. We utilized the Aminer(v13) dataset for the creation of the Knowledge Graph. We have introduced a novel scoring approach, “Node Importance Score” (NIS), for research papers using graph centrality measures i.e., PageRank, EigenVector Centrality, and Betweenness Centrality, which is combined with the H-Index of the publication of the paper. This score is used to find the top-k influential and important papers in a given domain. Validation of the obtained NIS shows that our approach overcomes certain flaws present in the state-of-the-art scoring system.