# We can provide a name for this pattern here.
pattern_name: exposure_with_input

# In 'classes', we define the terms we will use in this pattern. 
# In the OBO community the terms often have numeric IDs, so here 
# we can provide human-readable names we can use further in the pattern.
# The key is the name to be used; the value is the ID in prefixed form (i.e. a CURIE).
classes:
  exposure event: ExO:0000002
  Thing: owl:Thing

# Use 'relations' the same way as 'classes', 
# but for the object properties used in the pattern.
relations:
  has input: RO:0002233

# The 'vars' section defines the various slots that can be 
# filled in for this pattern. We have only one, which we call 'input'. 
# The value is the range, meaning the class of things that are valid 
# values for this pattern. By specifying owl:Thing, we're allowing any 
# class to be provided as a variable filler. You need a column in your 
# spreadsheet for each variable defined here, in addition to the `defined class` column.
vars:
  input: 'Thing'

# We can provide a template for an `rdfs:label` value to generate 
# for our new term. dosdp-tools will search the source ontology 
# to find the label for the filler term, and fill it into the 
# name template in place of the %s.
name:
  text: "exposure to %s"
  vars:
    - input

# This works the same as label generation, but instead creates
# a definition annotation.
def:
  text: "A exposure event involving the interaction of an exposure receptor to %s. Exposure may be through a variety of means, including through the air or surrounding medium, or through ingestion."
  vars:
    - input

# Here we can generate a logical axiom for our new concept. Create an 
# expression using OWL Manchester syntax. The expression can use any
# of the terms defined at the beginning of the pattern. A reference 
# to the variable value will be inserted in place of the %s.
equivalentTo: 
  text: "'exposure event' and 'has input' some %s"
  vars:
    - input