Published April 30, 2024 | Version CC-BY-NC-ND 4.0
Journal article Open

Missing Link Prediction in Art Knowledge Graph using Representation Learning

Creators

  • 1. College of Engineering, COEP Technological University Pune (Maharashtra), India.

Contributors

Contact person:

  • 1. College of Engineering, COEP Technological University Pune (Maharashtra), India.

Description

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.

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Additional details

Identifiers

DOI
10.35940/ijitee.J9264.13050424
EISSN
2278-3075

Dates

Accepted
2024-04-15
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.

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