A Physical Embedding Model for Knowledge Graphs This work was supported by the German Federal Ministry of Transport and Digital Infrastructure project OPAL (GA: 19F2028A) as well as the H2020 Marie Skłodowska-Curie project KnowGraphs (GA no. 860801).
Table 3: Cluster purity results. The best results are marked in bold. Experiments marked with * did not terminate after 24 hours of computation.
['[BOLD] Approach', '[BOLD] Drugbank', '[BOLD] DBpedia']
Pyke achieves a cluster purity of 0.75 on Drugbank and clearly outperforms all other approaches. DBpedia turned out to be a more difficult dataset. Still, Pyke was able to outperform all state-of-the-art approaches by between 11% and 26% (absolute) on Drugbank and between 9% and 23% (absolute) on DBpedia. Note that in 3 cases, the implementations available were unable to complete the computation of embeddings within 24 hours.