Version 1
: Received: 15 February 2022 / Approved: 17 February 2022 / Online: 17 February 2022 (11:49:24 CET)
How to cite:
Ur Rehman, E.; Saeed, A.; Minallah, N.; Hafeez, A. Knowledge Graph Embedding for Link Prediction Models. Preprints2022, 2022020212. https://doi.org/10.20944/preprints202202.0212.v1
Ur Rehman, E.; Saeed, A.; Minallah, N.; Hafeez, A. Knowledge Graph Embedding for Link Prediction Models. Preprints 2022, 2022020212. https://doi.org/10.20944/preprints202202.0212.v1
Ur Rehman, E.; Saeed, A.; Minallah, N.; Hafeez, A. Knowledge Graph Embedding for Link Prediction Models. Preprints2022, 2022020212. https://doi.org/10.20944/preprints202202.0212.v1
APA Style
Ur Rehman, E., Saeed, A., Minallah, N., & Hafeez, A. (2022). Knowledge Graph Embedding for Link Prediction Models. Preprints. https://doi.org/10.20944/preprints202202.0212.v1
Chicago/Turabian Style
Ur Rehman, E., Nasru Minallah and Abdul Hafeez. 2022 "Knowledge Graph Embedding for Link Prediction Models" Preprints. https://doi.org/10.20944/preprints202202.0212.v1
Abstract
For disciplines like biological science, security, and the medical field, link prediction is a popular research area. To demonstrate the link prediction many methods have been proposed. Some of them that have been demonstrated through this review paper are TransE, Complex, DistMult, and DensE models. Each model defines link prediction with different perceptions. We argue that the practical performance potential of these methods, having similar parameter values, using the fine-tuning technique to evaluate their reliability and reproducibility of results. We describe those methods and experiments; provide theoretical proofs and experimental examples, demonstrating how current link prediction methods work in such settings. We use the standard evaluation metrics for testing the model's ability.
Keywords
Knowledge Graphs; Link Prediction; Semantic-Based Models; Translation Based Embedded Models
Subject
Computer Science and Mathematics, Analysis
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
