Interpretable Graph Neural Networks for Reasoning and Relationship  Discovery in Scientific Knowledge Graphs

Pragya Pathak, Abhay Shukla and Somendra Tripathi

Interpretable Graph Neural Networks for Reasoning and Relationship Discovery in Scientific Knowledge Graphs

Authors

Pragya Pathak, Abhay Shukla and Somendra Tripathi

Keywords:

Knowledge Graphs, Graph Neural Networks, Interpretability, Link Prediction, DistMult, Explainable AI

Abstract

Scientific knowledge graphs (SKGs) have emerged as a powerful paradigm for representingstructured and relational knowledge across domains such as scientific publications, biomedical research, and semantic web systems. Despite the success of Graph Neural Networks

References

Bordes, A., Usunier, N., Garcia-Durán, A., Weston, J., & Yakhnenko, O. (2013). Translating embeddings for modeling multi-relational data. Advances in Neural Information Processing Systems, 26, 2787–2795.

Yang, B., Yih, W.-t., He, X., Gao, J., & Deng, L. (2015). Embedding entities and relations for learning and inference in knowledge bases. International Conference on Learning Representations (ICLR)

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Published

2024-12-20

How to Cite

Pragya Pathak, Abhay Shukla and Somendra Tripathi. (2024). Interpretable Graph Neural Networks for Reasoning and Relationship Discovery in Scientific Knowledge Graphs . Journal of Computational Analysis and Applications (JoCAAA), 33(08), 8284–8290. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5295