When Food meets AI: the Smart Recipe Project

Did you ever try a Maritozzo?

In the past post, we converted the recipe data, stored in JSON files, into RDF triples. In this post, we show you:

We already talked about the potentialities of connected data, but in practice, what can FoodGraph be used for?

Today we are overwhelmed by online recipe archives where we can easily find recipes that fit our requirements. However, connecting recipe data under a graph database structure…

When Food meets AI: the Smart Recipe Project

Delicious Cuttlefish

All knowledge is connected to all other knowledge. The fun is making the connections.

We learned from the previous articles the potentialities of enriched data and how it can feed ML and DL models to develop intelligent systems. We moved a step further connecting the data and the output of the extractor and classifier services (see the previous articles) under a graph database architecture. Graph databases represent an innovative, powerful approach to solve the problem of connected data in a way that is closer to how humans think about data.

Connected data matters

We live in a world made of…

When Food meets AI: the Smart Recipe Project

Mussel soup

Since its release in late 2018, the Bidirectional Encoder Representations from Transformer, aka BERT, has entered the NLP model hall of fame awarding the nomination of the state-of-the-art in various NLP tasks.

Out of metaphors, BERT has positively changed the way to face NLP tasks, solving many challenging problems in the NLP field.

Given its fame, the post does not dive into BERT hidden magic (others have done it brilliantly), but rather it shows how BERT was exploited in the Smart Recipe Project. Here we developed a system able to identify the ingredient taxonomic class. …

When Food meets AI: the Smart Recipe Project

The great Carbonara

In the previous articles, we constructed two label datasets to train machine learning models. The aim is to develop systems able to interpret and extract information from recipes. We categorized these systems into four main categories: extractors, classifiers, regressors, and searchers.

The post explores the extractor services used to extract information (ingredients, quantities, time of preparation, etc) from recipes. To achieve that, we used Named Entity Recognition (NER).

What you will find in the article:

  • NER: the task and its main applications.
  • An overview of NER approaches.
  • NER for the Smart Recipe Project.
  • Results and next steps.

NER is a…

When Food meets AI: the Smart Recipe Project

Tiramisù, a classic recipe

In the previous article, we described how to preprocess a dataset of recipes. The next step is to enrich its data with extra information. The goal? The construction of a food-entity tagged dataset to exploit in several and different Information Retrieval (IR) tasks.

Who works on ML projects should have no doubt about the utility of such a resource. Indeed, the unavailability or scarcity of training data is one of the most serious challenges in ML and specifically in NLP. A problem that gets harder when the data you need has to be labeled. …

When Food meets AI: the Smart Recipe Project

Oscar Wilde said “I can’t stand people who do not take food seriously” and we totally agree with him. ​Food is one of the essential things we experience every day and not just because it is our main source of survival. Cooking recipes, videos, photos are everywhere on the web, which is today the greatest archive of food-related content.

The new cover of “La Cucina Italiana”

But what if this big amount of data meets Artificial Intelligence? The Smart Recipe Project, born from the ​cooperation between the global media company ​Condé Nast​, and the IT company ​RES answered this question developing ​AI services able to extract information…

Conde Nast Italy

Condé Nast Italia è una multimedia communication company che raggiunge un’audience profilata grazie alle numerose properties omnichannel.

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