Update README.md

README.md

@@ -28,9 +28,21 @@ are answered 'no' when the expected answer is 'yes' (or viceversa), the knowledg
 It is our experience that incomplete clusters are very few (less than 3%) meaning that the LLMs we have tested know about the domains included here (see below for a list of the
 individual datasets), as opposed to inconsistent clusters, which can be between 6%-20% of the total clusters. 
 
+The image below indicates the types of edges the query clusters are supposed to test. It is worth noting that these correspond to common sense axioms about conceptualization, like
+the fact that subConceptOf is transitive (4) or that subconcepts inherit the properties of their parent concepts (5). These axioms are listed in the accompanying paper (see below) 
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+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c80841d418013c77d9f1cd/QzHLVAn_76xJ2qPgeIwSk.png)
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 ## How it is made: 
 The questions and clusters are automatically generated from a knowledge graph from seed concepts and properties. In our case, we have used Wikidata, 
 a well known knowledge graph. The result is an RDF/OWL subgraph that can be queried and reasoned over using Semantic Web technology. 
+The figure below summarizes the steps used. The last two steps refer to a possible use case for this dataset, including using in-context learning to improve the 
+performance of the dataset. 
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c80841d418013c77d9f1cd/7WX4LExD1ArTwTsKQxi8x.png)
 
 ## Types of query clusters