Monitoring semantic relatedness and revealing fairness and biases through trend tests
An emerging application domain concerning content-based recommender systems provides a better consideration of the semantics behind textual descriptions. Traditional approaches often miss relevant information due to their sole focus on syntax. However, the Semantic Web community has enriched resourc...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
|
| Series: | International Journal of Information Management Data Insights |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667096824000946 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846150123775590400 |
|---|---|
| author | Jean-Rémi Bourguet Adama Sow |
| author_facet | Jean-Rémi Bourguet Adama Sow |
| author_sort | Jean-Rémi Bourguet |
| collection | DOAJ |
| description | An emerging application domain concerning content-based recommender systems provides a better consideration of the semantics behind textual descriptions. Traditional approaches often miss relevant information due to their sole focus on syntax. However, the Semantic Web community has enriched resources with cultural and linguistic background knowledge, offering new standards for word categorization. This paper proposes a framework that combines the information extractor ReVerb with the WordNet taxonomy to monitor global semantic relatedness scores. Additionally, an experimental validation confronts human-based semantic relatedness scores with theoretical ones, employing Mann–Kendall trend tests to reveal fairness and biases. Overall, our framework introduces a novel approach to semantic relatedness monitoring by providing valuable insights into fairness and biases. |
| format | Article |
| id | doaj-art-b3bc1cad9ee34f93b997537db3f56876 |
| institution | Kabale University |
| issn | 2667-0968 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Information Management Data Insights |
| spelling | doaj-art-b3bc1cad9ee34f93b997537db3f568762024-11-29T06:25:23ZengElsevierInternational Journal of Information Management Data Insights2667-09682025-06-0151100305Monitoring semantic relatedness and revealing fairness and biases through trend testsJean-Rémi Bourguet0Adama Sow1Departmento de Ciência da Computação, Universidade Vila Velha (UVV), Avenida Comissário José Dantas de Melo, 21 - Boa Vista II, CEP 29102-920 Vila Velha, Brazil; Dipartimento di Scienze Politiche, Scienze della Comunicazione ed Ingegneria dell’Informazione, Università degli Studi di Sassari (UNISS), Viale Mancini 5 07100 Sassari, Italy; Corresponding author at: Departmento de Ciência da Computação, Universidade Vila Velha (UVV), Avenida Comissário José Dantas de Melo, 21 - Boa Vista II, CEP 29102-920 Vila Velha, Brazil.Département Génie Informatique et Télécommunications, Ecole Polytechnique de Thiès (EPT), Route Base aérienne Diakhao BP 64551 Dakar-Fann, Senegal; Department of Computer Science and Software Engineering, Université de Laval 1065 avenue de la Médecine, G1K 7P4 Quebec, CanadaAn emerging application domain concerning content-based recommender systems provides a better consideration of the semantics behind textual descriptions. Traditional approaches often miss relevant information due to their sole focus on syntax. However, the Semantic Web community has enriched resources with cultural and linguistic background knowledge, offering new standards for word categorization. This paper proposes a framework that combines the information extractor ReVerb with the WordNet taxonomy to monitor global semantic relatedness scores. Additionally, an experimental validation confronts human-based semantic relatedness scores with theoretical ones, employing Mann–Kendall trend tests to reveal fairness and biases. Overall, our framework introduces a novel approach to semantic relatedness monitoring by providing valuable insights into fairness and biases.http://www.sciencedirect.com/science/article/pii/S2667096824000946Semantic relatednessFairnessBiasesWordNetReVerbVisualization |
| spellingShingle | Jean-Rémi Bourguet Adama Sow Monitoring semantic relatedness and revealing fairness and biases through trend tests International Journal of Information Management Data Insights Semantic relatedness Fairness Biases WordNet ReVerb Visualization |
| title | Monitoring semantic relatedness and revealing fairness and biases through trend tests |
| title_full | Monitoring semantic relatedness and revealing fairness and biases through trend tests |
| title_fullStr | Monitoring semantic relatedness and revealing fairness and biases through trend tests |
| title_full_unstemmed | Monitoring semantic relatedness and revealing fairness and biases through trend tests |
| title_short | Monitoring semantic relatedness and revealing fairness and biases through trend tests |
| title_sort | monitoring semantic relatedness and revealing fairness and biases through trend tests |
| topic | Semantic relatedness Fairness Biases WordNet ReVerb Visualization |
| url | http://www.sciencedirect.com/science/article/pii/S2667096824000946 |
| work_keys_str_mv | AT jeanremibourguet monitoringsemanticrelatednessandrevealingfairnessandbiasesthroughtrendtests AT adamasow monitoringsemanticrelatednessandrevealingfairnessandbiasesthroughtrendtests |