# Apport des ontologies pour le calcul de la similarit'e s'emantique au sein d'un syst`eme de recommandation

arXiv: https://arxiv.org/abs/2205.12539 · HTML: https://mlsanity.com/paper/2205.12539/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 34 references that are in the map.
- First posted on arXiv 25 May 2022.

## Builds on (most cited first, 4 of 4)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [FastText.zip: Compressing text classification models](https://mlsanity.com/paper/1612.03651/) (2016)
- [Evaluation Of Word Embeddings From Large-Scale French Web Content](https://mlsanity.com/paper/2105.01990/) (2021)

## Built on by (most cited first, 2 of 2)

- [Combining Embedding-Based and Semantic-Based Models for Post-hoc Explanations in Recommender Systems](https://mlsanity.com/paper/2401.04474/) (2024)
- [A Personalized Recommender System Based-on Knowledge Graph Embeddings](https://mlsanity.com/paper/2307.10680/) (2023)

## Abstract (arXiv)

Measurement of the semantic relatedness or likeness between terms, words, or text data plays an important role in different applications dealing with textual data such as knowledge acquisition, recommender system, and natural language processing. Over the past few years, many ontologies have been developed and used as a form of structured representation of knowledge bases for information systems. The calculation of semantic similarity from ontology has developed and depending on the context is complemented by other similarity calculation methods. In this paper, we propose and carry on an approach for the calculation of ontology-based semantic similarity using in the context of a recommender system.