Missing Link Prediction in Art Knowledge Graph using Representation Learning
Swapnil S. Mahure1, Anish R. Khobragade2

1Swapnil S. Mahure, College of Engineering, COEP Technological University Pune (Maharashtra), India.

2Anish R. Khobragade, College of Engineering, COEP Technological University Pune (Maharashtra), India.  

Manuscript received on 18 August 2022 | Revised Manuscript received on 03 September 2022 | Manuscript Accepted on 15 April 2024 | Manuscript published on 30 April 2024 | PP: 30-33 | Volume-13 Issue-5, April 2024 | Retrieval Number: 100.1/ijitee.J926409111022 | DOI: 10.35940/ijitee.J9264.13050424

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Abstract: 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.

Keywords: KG Embeddings, Artwork, Link Prediction, Neural Network
Scope of the Article: Neural Information Processing