Update README

Updates the READMEs to change the examples to MONDO links instead of EFO links, since those often contain bugs

README-UI.md

@@ -9,7 +9,7 @@ The following information pertains to the text2term UI, which is written [here](
 -   npm >= 8.0.0
 -   Python >= 3.9.0
 -   pip >= 21.0.0
--   text2term >= 1.1.0
+-   text2term >= 4.1.2
 
 **\*** These are the versions I have that work; while I know Python 3.9 or higher is necessary, the others may not strictly require the listed versions.
 

README.md

@@ -13,40 +13,40 @@ pip install text2term
 import text2term
 import pandas
 
-df1 = text2term.map_terms("test/unstruct_terms.txt", "http://www.ebi.ac.uk/efo/efo.owl")
-df2 = text2term.map_terms(["asthma", "acute bronchitis"], "http://www.ebi.ac.uk/efo/efo.owl")
-df3 = text2term.map_terms({"asthma":"disease", "acute bronchitis":["disease", "lungs"]}, "http://www.ebi.ac.uk/efo/efo.owl")
+df1 = text2term.map_terms("test/unstruct_terms.txt", "http://purl.obolibrary.org/obo/mondo.owl")
+df2 = text2term.map_terms(["asthma", "acute bronchitis"], "http://purl.obolibrary.org/obo/mondo.owl")
+df3 = text2term.map_terms({"asthma":"disease", "acute bronchitis":["disease", "lungs"]}, "http://purl.obolibrary.org/obo/mondo.owl")
 ```
 Below is an example of caching, assuming the same imports as above:
 ```python
-text2term.cache_ontology("http://www.ebi.ac.uk/efo/efo.owl", "EFO")
-df1 = text2term.map_terms("test/unstruct_terms.txt", "EFO", use_cache=True)
-df2 = text2term.map_terms(["asthma", "acute bronchitis"], "EFO", use_cache=True)
-text2term.clear_cache("EFO")
+text2term.cache_ontology("http://purl.obolibrary.org/obo/mondo.owl", "MONDO")
+df1 = text2term.map_terms("test/unstruct_terms.txt", "MONDO", use_cache=True)
+df2 = text2term.map_terms(["asthma", "acute bronchitis"], "MONDO", use_cache=True)
+text2term.clear_cache("MONDO")
 ```
 
 ### Command Line
 The basic use of the tool requires a `source` file containing a list of terms to map to the given `target` ontology:  
-`python text2term -s test/unstruct_terms.txt -t http://www.ebi.ac.uk/efo/efo.owl`
+`python text2term -s test/unstruct_terms.txt -t http://purl.obolibrary.org/obo/mondo.owl`
 
 Specify an output file where the mappings should be saved using `-o`:  
-`python text2term -s test/unstruct_terms.txt -t efo.owl -o /Documents/my-mappings.csv`
+`python text2term -s test/unstruct_terms.txt -t mondo.owl -o /Documents/my-mappings.csv`
 
 Set the minimum acceptable similarity score for mapping each given term to an ontology term using `-min`:  
-`python text2term -s test/unstruct_terms.txt -t efo.owl -min 0.8`  
+`python text2term -s test/unstruct_terms.txt -t mondo.owl -min 0.8`  
 The mapped terms returned will have been determined to be 0.8 similar to their source terms in a 0-1 scale.  
 
 Exclude deprecated ontology terms (declared as such via *owl:deprecated true*) using `-d`:  
-`python text2term -s test/unstruct_terms.txt -t efo.owl -d`
+`python text2term -s test/unstruct_terms.txt -t mondo.owl -d`
 
 Limit search to only terms whose IRIs start with any IRI given in a list specified using `-iris`:  
-`python text2term.py -s test/unstruct_terms.txt -t efo.owl -iris http://www.ebi.ac.uk/efo/EFO,http://purl.obolibrary.org/obo/HP`  
+`python text2term.py -s test/unstruct_terms.txt -t mondo.owl -iris http://purl.obolibrary.org/obo/mondo.owl,http://purl.obolibrary.org/obo/HP`  
 Here, because EFO reuses terms from other ontologies such as HP and GO, the HP terms would be included but the GO terms would be excluded.
 
 Use the cache on the command line, first by flagging it, then in the future using the acronym:
-`python text2term -s test/unstruct_terms.txt -t http://www.ebi.ac.uk/efo/efo.owl -c EFO`
+`python text2term -s test/unstruct_terms.txt -t http://purl.obolibrary.org/obo/mondo.owl -c MONDO`
 Then, after running this, the following command is equivalent:
-`python text2term -s test/unstruct_terms.txt -t EFO`
+`python text2term -s test/unstruct_terms.txt -t MONDO`
 
 ## Programmatic Usage
 The tool can be executed in Python with the `map_terms` function: