diff --git a/sigs/xai/README.md b/sigs/xai/README.md
index c57c64a..a759acc 100644
--- a/sigs/xai/README.md
+++ b/sigs/xai/README.md
@@ -77,13 +77,13 @@ Here we adopt a computer-scientific perspective and define knowledge as informat
 
 <div align=center><img src="http://image.huawei.com/tiny-lts/v1/images/7d2398dac9820bece729f7abdc41a3bc_694x277.png"></div>
 
-< center>Figure 2. An overview of knowledge-enhanced machine learning [4]< /center>
+<div align=center>Figure 2. An overview of knowledge-enhanced machine learning [4]</div>
 
 Based on the three above analysis questions about knowledge source, representation, and integration, this taxonomy serves as a classification framework for informed machine learning.
 
 <div align=center><img src="http://image.huawei.com/tiny-lts/v1/images/521f515df9c6994da54d919a32e36ee0_686x452.png"></div>
 
-< center>Figure 3. A taxonomy of knowledge-enhanced machine learning [4]< /center>
+<div align=center>Figure 3. A taxonomy of knowledge-enhanced machine learning [4]</div>
 
 
 Here we give a first conceptual overview of the knowledge representation types, and for each knowledge representation, we collect the informed machine learning approaches and present the observed (paths from) knowledge source and the observed (paths to) knowledge integration.