  Traditional cooking recipes follow a structure which can be modelled very
well if the rules and semantics of the different sections of the recipe text
are analyzed and represented accurately. We propose a structure that can
accurately represent the recipe as well as a pipeline to infer the best
representation of the recipe in this uniform structure. The Ingredients section
in a recipe typically lists down the ingredients required and corresponding
attributes such as quantity, temperature, and processing state. This can be
modelled by defining these attributes and their values. The physical entities
which make up a recipe can be broadly classified into utensils, ingredients and
their combinations that are related by cooking techniques. The instruction
section lists down a series of events in which a cooking technique or process
is applied upon these utensils and ingredients. We model these relationships in
the form of tuples. Thus, using a combination of these methods we model cooking
recipe in the dataset RecipeDB to show the efficacy of our method. This mined
information model can have several applications which include translating
recipes between languages, determining similarity between recipes, generation
of novel recipes and estimation of the nutritional profile of recipes. For the
purpose of recognition of ingredient attributes, we train the Named Entity
Relationship (NER) models and analyze the inferences with the help of K-Means
clustering. Our model presented with an F1 score of 0.95 across all datasets.
We use a similar NER tagging model for labelling cooking techniques (F1 score =
0.88) and utensils (F1 score = 0.90) within the instructions section. Finally,
we determine the temporal sequence of relationships between ingredients,
utensils and cooking techniques for modeling the instruction steps.
