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I'm trying to use PyTorch BigGraph pre-trained embeddings of Wikidata items for disambiguation. The problem is that the results I am getting by using dot (or cosine) similarity are not great. For example, the similarity between the Python programming language and the snake with the same name is greater than between Python and Django. Does anybody know if there is a Wikidata embedding that results in better similarities? The only alternative I've found is Webmembedder embeddings but they are incomplete.

Wiki Item 1 Wiki Item 2 dot cosine
Q28865 (Python language) Q271218 (Python snake) 17.625 0.64013671875
Q28865 (Python language) Q10811 (Reptiles) 8.21875 0.300048828125
Q28865 (Python language) Q2407 (C++) 25.296875 0.919921875
Q28865 (Python language) Q842014 (Django Python) 11.34375 0.409912109375
Q271218 (Python snake) Q10811 (Reptiles) 11.25 0.409912109375
Q271218 (Python snake) Q2407 (C++) 12.5390625 0.4599609375
Q271218 (Python snake) Q842014 (Django Python) 6.05859375 0.219970703125
Q10811 (Reptils) Q2407 (C++) 4.76171875 0.1700439453125
Q10811 (Reptils) Q842014 (Django Python) -0.60009765625 -0.0200042724609375
Q2407 (C++) Q842014 (Django Python) 11.53125 0.419921875
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Haven't checked it personally yet, but here you can have some more embeddings generated from the Wikidata5m dataset (based on Wikidata and Wikipedia) using TransE, DistMult, ComplEx, SimplE, RotatE, and QuatE.

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