# Improving Semantic Similarity Measure Within a Recommender System Based-on RDF Graphs

arXiv: https://arxiv.org/abs/2307.10639 · HTML: https://mlsanity.com/paper/2307.10639/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2023.

## Built on by (most cited first, 5 of 5)

- [Combining Embedding-Based and Semantic-Based Models for Post-hoc Explanations in Recommender Systems](https://mlsanity.com/paper/2401.04474/) (2024)
- [Advances and Challenges in Semantic Textual Similarity: A Comprehensive Survey](https://mlsanity.com/paper/2601.03270/) (2025)
- [Development of Ontological Knowledge Bases by Leveraging Large Language Models](https://mlsanity.com/paper/2601.10436/) (2026)
- [From Individual to Group: Developing a Context-Aware Multi-Criteria Group Recommender System](https://mlsanity.com/paper/2503.22752/) (2025)
- [Enhancing Recommender Systems Using Textual Embeddings from Pre-trained Language Models](https://mlsanity.com/paper/2504.08746/) (2025)

## Abstract (arXiv)

In today's era of information explosion, more users are becoming more reliant upon recommender systems to have better advice, suggestions, or inspire them. The measure of the semantic relatedness or likeness between terms, words, or text data plays an important role in different applications dealing with textual data, as in a recommender system. 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 measure of semantic similarity from ontology has developed by several methods. In this paper, we propose and carry on an approach for the improvement of semantic similarity calculations within a recommender system based-on RDF graphs.