From 882f28f2709bcb841abdfb6650a8d1ddb4603407 Mon Sep 17 00:00:00 2001
From: Claire <143529280+ClaireSuperlinked@users.noreply.github.com>
Date: Fri, 5 Jan 2024 15:56:40 +0000
Subject: [PATCH] Update knowledge_graph_embedding.md

trying to fix math
---
 docs/use_cases/knowledge_graph_embedding.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/use_cases/knowledge_graph_embedding.md b/docs/use_cases/knowledge_graph_embedding.md
index a28665f82..8eb306f0b 100644
--- a/docs/use_cases/knowledge_graph_embedding.md
+++ b/docs/use_cases/knowledge_graph_embedding.md
@@ -36,7 +36,7 @@ KGE algorithms vary in the similarity functions they employ, and how they define
 
 ## Demo using DistMult KGE
 
-For our KGE model demo, we opted for the DistMult KGE algorithm. It works by representing the likelihood of relationships between entities (i.e., similarity) as a _bilinear_ function. Essentially, DisMult KGE assumes that the score of a given triple (comprised of a head entity $h$, a relationship $r$, and a tail entity $t$) can be computed as: $h^T \text{diag}(r) t$. 
+For our KGE model demo, we opted for the DistMult KGE algorithm. It works by representing the likelihood of relationships between entities (i.e., similarity) as a _bilinear_ function. Essentially, DisMult KGE assumes that the score of a given triple (comprised of a head entity \mathnormal{h}, a relationship \varUpsilon, and a tail entity \mathnormal{t}) can be computed as: \mathnormal{h}^{T} \diag(\varUpsilon) \mathnormal{t}. 
 
 ![DistMult similarity function](../assets/use_cases/knowledge_graph_embedding/distmult.png)