Update README.md

README.md

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 ### Dataset Summary
 
 OntoLAMA is a set of language model (LM) probing datasets for ontology subsumption inference. 
-The work follows the "LMs-as-KBs" literature but focuses on conceptualised knowledge extracted from formalised KBs such as the OWL ontologies. 
-Specifically, the subsumption inference (SI) task is introduced and formulated in the NLI style, where the sub-concept and the super-concept 
-involved in a subsumption axiom are verbalised and fitted into a template to form the premise and hypothesis, respectively. The SI task is 
-further divided into Atomic SI and Complex SI where the former involves only atomic named concepts and the latter involves both atomic and complex concepts. 
-Real-world ontologies of different scales and domains are used for constructing OntoLAMA and in total there are four Atomic SI datasets and two Complex SI datasets.
+The work follows the "LMs-as-KBs" literature but focuses on conceptualised knowledge extracted from formalised KBs 
+such as the OWL ontologies. Specifically, the subsumption inference (SI) task is introduced and formulated in the
+Natural Language Inference (NLI) style, where the sub-concept and the super-concept involved in a subsumption 
+axiom are verbalised and fitted into a template to form the premise and hypothesis, respectively. 
+The sampled axioms are verified through ontology reasoning. The SI task is further divided into Atomic SI and 
+Complex SI where the former involves only atomic named concepts and the latter involves both atomic and complex concepts. 
+Real-world ontologies of different scales and domains are used for constructing OntoLAMA and in total there are four Atomic 
+SI datasets and two Complex SI datasets.
 
 ### Languages