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Enriching Word Embeddings with Food Knowledge for Ingredient Retrieval

Authors Álvaro Mendes Samagaio , Henrique Lopes Cardoso , David Ribeiro

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Author Details

Álvaro Mendes Samagaio
  • Faculty of Engineering, University of Porto, Portugal
  • Fraunhofer Portugal, Porto, Portugal
Henrique Lopes Cardoso
  • Faculty of Engineering, University of Porto, Portugal
  • Artificial Intelligence and Computer Science Laboratory (LIACC), Porto, Portugal
David Ribeiro
  • Fraunhofer Portugal, Porto, Portugal

Funding

  • Lopes Cardoso, Henrique: This research is partially supported by LIACC (FCT/UID/CEC/0027/2020), funded by Fundação para a Ciência e a Tecnologia (FCT).

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Álvaro Mendes Samagaio, Henrique Lopes Cardoso, and David Ribeiro. Enriching Word Embeddings with Food Knowledge for Ingredient Retrieval. In 3rd Conference on Language, Data and Knowledge (LDK 2021). Open Access Series in Informatics (OASIcs), Volume 93, pp. 15:1-15:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/OASIcs.LDK.2021.15

Abstract

Smart assistants and recommender systems must deal with lots of information coming from different sources and having different formats. This is more frequent in text data, which presents increased variability and complexity, and is rather common for conversational assistants or chatbots. Moreover, this issue is very evident in the food and nutrition lexicon, where the semantics present increased variability, namely due to hypernyms and hyponyms. This work describes the creation of a set of word embeddings based on the incorporation of information from a food thesaurus - LanguaL - through retrofitting. The ingredients were classified according to three different facet label groups. Retrofitted embeddings seem to properly encode food-specific knowledge, as shown by an increase on accuracy as compared to generic embeddings (+23%, +10% and +31% per group). Moreover, a weighing mechanism based on TF-IDF was applied to embedding creation before retrofitting, also bringing an increase on accuracy (+5%, +9% and +5% per group). Finally, the approach has been tested with human users in an ingredient retrieval exercise, showing very positive evaluation (77.3% of the volunteer testers preferred this method over a string-based matching algorithm).

Subject Classification

ACM Subject Classification
  • Computing methodologies → Artificial intelligence
  • Computing methodologies → Knowledge representation and reasoning
  • Computing methodologies → Lexical semantics
Keywords
  • Word embeddings
  • Retrofitting
  • LanguaL
  • Food Embeddings
  • Knowledge Graph

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