3d8d135818d1675848a1fa9 20240426000626185 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 04 30 2024 13 5 College of Engineering, COEP Technological University Pune (Maharashtra), India. Swapnil S. Mahure Anish R. Khobragade College of Engineering, COEP Technological University Pune (Maharashtra), India. Knowledge graphs are an important evolving field in Artificial Intelligence domain which has multiple applications such as in question answering, important information retrieval, information recommendation, Natural language processing etc. Knowledge graph has one big limitation i.e. Incompleteness, it is due to because of real world data are dynamic and continues evolving. This incompleteness of Knowledge graph can be overcome or minimized by using representation learning models. There are several models which are classified on the base of translation distance, semantic information and NN (Neural Network) based. Earlier the various embedding models are test on mostly two well-known datasets WN18RR & FB15k-237. In this paper, new dataset i.e. ArtGraph has been utilised for link prediction using representation learning models to enhance the utilization of ArtGraph in various domains. Experimental results shown ConvKB performed better over the other models for link prediction task. 04 30 2024 30 33 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true International Journal of Innovative Technology and Exploring Engineering (IJITEE) No, I did not receive. No conflicts of interest to the best of our knowledge. No, the article does not require ethical approval and consent to participate with evidence. Not relevant. All authors have equal participation in this article. 10.35940/ijitee.J9264.13050424 https://www.ijitee.org/portfolio-item/J926409111022/