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Food is not only a basic human necessity but also a key factor driving asociety's health and economic well-being. As a result, the cooking domain is apopular use-case to demonstrate decision-support (AI) capabilities in serviceof benefits like precision health with tools ranging from information retrievalinterfaces to task-oriented chatbots. An AI here should understand concepts inthe food domain (e.g., recipes, ingredients), be tolerant to failuresencountered while cooking (e.g., browning of butter), handle allergy-basedsubstitutions, and work with multiple data modalities (e.g. text and images).However, the recipes today are handled as textual documents which makes itdifficult for machines to read, reason and handle ambiguity. This demands aneed for better representation of the recipes, overcoming the ambiguity andsparseness that exists in the current textual documents. In this paper, wediscuss the construction of a machine-understandable rich recipe representation(R3), in the form of plans, from the recipes available in natural language. R3is infused with additional knowledge such as information about allergens andimages of ingredients, possible failures and tips for each atomic cooking step.To show the benefits of R3, we also present TREAT, a tool for recipe retrievalwhich uses R3 to perform multi-modal reasoning on the recipe's content (planobjects - ingredients and cooking tools), food preparation process (planactions and time), and media type (image, text). R3 leads to improved retrievalefficiency and new capabilities that were hither-to not possible in textualrepresentation.

A Rich Recipe Representation as Plan to Support Expressive Multi Modal Queries on Recipe Content and Preparation Process