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Global sensitivity analysis of knowledge graph embedding model hyperparameters

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Assessing the Effects of Method Hyperparameters on Knowledge Graph Embedding Quality

This repository contains the data and code used the analysis. Study was led by Oliver Lloyd as part of a PhD thesis supervised by Tom Gaunt, Yi Liu, and Patrick Rubin-Delanchy.

UMLS-43 is a variant of the UMLS knowledge graph that is robust to data leakage through inverse relations. It has been derived by removing three edge types that should be considered problematic by Dettmers' definition: 'degree_of', 'precedes', and 'derivative_of'. It is presented here as a .tsv edgelist, such that each line represents one edge in the (head, relation, tail) format.

Contact: [email protected].

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Global sensitivity analysis of knowledge graph embedding model hyperparameters

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