Food Ontology Enrichment Using Word Embeddings and Machine Learning Technologies

Melissa Oussaid, Farida Bouarab-Dahmani, N. Cullot
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引用次数: 1

Abstract

The emergence of the Internet has made available a large amount of food data in different formats. Therefore, manual relevant data extraction for food ontology population and enrichment has become a complex process. The automation of the knowledge extraction task offers significant opportunities to overcome several manual process limitations, such as complexity (time-consuming and resource-intense). In this paper, we propose a new approach that aims at the automated extraction of new ontological concepts from unstructured data to enrich a food ontology. For this purpose, an ontology and a corpus of food data have been built. This data is used to train the Word2Vec model. Then, a measure of similarity based on word embedding is done. New entities are selected as candidates according to the result of similarity scores and are used to generate new concepts. The obtained results showed the effectiveness of our proposal, with a precision score of 78%.
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利用词嵌入和机器学习技术丰富食品本体
互联网的出现使得大量不同格式的食品数据成为可能。因此,人工对食品本体进行相关数据的提取和充实已成为一个复杂的过程。知识提取任务的自动化为克服一些手动过程限制提供了重要的机会,例如复杂性(耗时和资源密集)。在本文中,我们提出了一种新的方法,旨在从非结构化数据中自动提取新的本体概念,以丰富食品本体。为此,建立了食品数据本体和语料库。这些数据用于训练Word2Vec模型。然后,基于词嵌入的相似性度量。根据相似性得分的结果选择新的实体作为候选实体,并用于生成新的概念。得到的结果表明,我们的建议是有效的,精度得分为78%。
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