mlsanityData to 7 Oct 2026

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

arXiv 2205.12539 ↗First posted 25 May 2022cs.IR cs.AIcited by 2

Not on the map. Why?

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 4 of its 34 references that are in the map. First posted on arXiv 25 May 2022.

Builds on

4 of 34 references.

References · 4

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  3. 2016FastText.zip: Compressing text classification modelscited by 331
  4. 2021Evaluation Of Word Embeddings From Large-Scale French Web Contentcited by 3

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 1.

2 papers.