Toward Robust Food Ontology Mapping
Riste Stojanov, Ilija Kocev, Sasho Gramatikov, Gorjan Popovski, Barbara Koroušić Seljak, Tome Eftimov · 2020
Data normalization methodologies are extremely welcome to link extracted information from textual data to different semantic resources. These methodologies have been previously well researched especially in the biomedical domain, where health concepts were normalized and described using semantic tags. Recently, a methodology for normalizing food concepts has been proposed, based on Named-Entity Recognition methods resulting in the FoodOntoMap semantic resource. In this paper, we propose and evaluate a new architecture for linking phrases (i.e. textual name for foods) to concepts from semantic resources in the Food and Nutrition domain. We represent the food phrases (i.e. their textual name) in continuous vector space using state-of-the-art Natural Language Processing (NLP) embedding algorithms, and evaluate their proximity with respect to the annotated semantic food concepts. Additionally, indexing was incorporated to improve efficiency.The GloVe embedding with mean pooling provided best evaluation results, with maximum recall of 74% for the Snomed CT semantic dataset, which is promising result, but also opens a space for future improvement of the phrase representations, and their incorporation in this system.