A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text
Ontologies are used to model knowledge in several domains of interest, such as the biomedical domain. Conceptualization is the basic task for ontology building. Concepts are identified, and then they are linked through their semantic relationships. Recently, ontologies have constituted a crucial par...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| منشور في: |
2019
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://bspace.buid.ac.ae/handle/1234/2988 https://doi.org/10.3390/a12090182. |
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| _version_ | 1862980612046979072 |
|---|---|
| author | M. Ghoniem , Rania |
| author2 | Alhelwa, Nawal Shaalan , Khaled |
| author2_role | author author |
| author_facet | M. Ghoniem , Rania Alhelwa, Nawal Shaalan , Khaled |
| author_role | author |
| dc.creator.none.fl_str_mv | M. Ghoniem , Rania Alhelwa, Nawal Shaalan , Khaled |
| dc.date.none.fl_str_mv | 2019 2025-05-13T13:35:35Z 2025-05-13T13:35:35Z |
| dc.identifier.none.fl_str_mv | Rania M. Ghoniem, Nawal Alhelwa and Khaled Shaalan (2019) “A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text,” Algorithms, 12(9), p. 182. https://bspace.buid.ac.ae/handle/1234/2988 https://doi.org/10.3390/a12090182. |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | MDPI |
| dc.relation.none.fl_str_mv | Algorithmsv12 n9 (20190801): 182 |
| dc.subject.none.fl_str_mv | text mining; ontology learning; hybrid models; genetic algorithms; whale optimization algorithm |
| dc.title.none.fl_str_mv | A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text |
| dc.type.none.fl_str_mv | Article |
| description | Ontologies are used to model knowledge in several domains of interest, such as the biomedical domain. Conceptualization is the basic task for ontology building. Concepts are identified, and then they are linked through their semantic relationships. Recently, ontologies have constituted a crucial part of modern semantic webs because they can convert a web of documents into a web of things. Although ontology learning generally occupies a large space in computer science, Arabic ontology learning, in particular, is underdeveloped due to the Arabic language’s nature as well as the profundity required in this domain. The previously published research on Arabic ontology learning from text falls into three categories: developing manually hand-crafted rules, using ordinary supervised/unsupervised machine learning algorithms, or a hybrid of these two approaches. The model proposed in this work contributes to Arabic ontology learning in two ways. First, a text mining algorithm is proposed for extracting concepts and their semantic relations from text documents. The algorithm calculates the concept frequency weights using the term frequency weights. Then, it calculates the weights of concept similarity using the information of the ontology structure, involving (1) the concept’s path distance, (2) the concept’s distribution layer, and (3) the mutual parent concept’s distribution layer. Then, feature mapping is performed by assigning the concepts’ similarities to the concept features. Second, a hybrid genetic-whale optimization algorithm was proposed to optimize ontology learning from Arabic text. The operator of the G-WOA is a hybrid operator integrating GA’s mutation, crossover, and selection processes with the WOA’s processes (encircling prey, attacking of bubble-net, and searching for prey) to fulfill the balance between both exploitation and exploration, and to find the solutions that exhibit the highest fitness. For evaluating the performance of the ontology learning approach, extensive comparisons are conducted using different Arabic corpora and bio-inspired optimization algorithms. Furthermore, two publicly available non-Arabic corpora are used to compare the efficiency of the proposed approach with those of other languages. The results reveal that the proposed genetic-whale optimization algorithm outperforms the other compared algorithms across all the Arabic corpora in terms of precision, recall, and F-score measures. Moreover, the proposed approach outperforms the state-of-the-art methods of ontology learning from Arabic and non-Arabic texts in terms of these three measures. |
| id | budr_afa30e2d7d52e01e871d246dba7dd47e |
| identifier_str_mv | Rania M. Ghoniem, Nawal Alhelwa and Khaled Shaalan (2019) “A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text,” Algorithms, 12(9), p. 182. |
| language_invalid_str_mv | en |
| network_acronym_str | budr |
| network_name_str | The British University in Dubai repository |
| oai_identifier_str | oai:bspace.buid.ac.ae:1234/2988 |
| publishDate | 2019 |
| publisher.none.fl_str_mv | MDPI |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic TextM. Ghoniem , RaniaAlhelwa, NawalShaalan , Khaledtext mining; ontology learning; hybrid models; genetic algorithms; whale optimization algorithmOntologies are used to model knowledge in several domains of interest, such as the biomedical domain. Conceptualization is the basic task for ontology building. Concepts are identified, and then they are linked through their semantic relationships. Recently, ontologies have constituted a crucial part of modern semantic webs because they can convert a web of documents into a web of things. Although ontology learning generally occupies a large space in computer science, Arabic ontology learning, in particular, is underdeveloped due to the Arabic language’s nature as well as the profundity required in this domain. The previously published research on Arabic ontology learning from text falls into three categories: developing manually hand-crafted rules, using ordinary supervised/unsupervised machine learning algorithms, or a hybrid of these two approaches. The model proposed in this work contributes to Arabic ontology learning in two ways. First, a text mining algorithm is proposed for extracting concepts and their semantic relations from text documents. The algorithm calculates the concept frequency weights using the term frequency weights. Then, it calculates the weights of concept similarity using the information of the ontology structure, involving (1) the concept’s path distance, (2) the concept’s distribution layer, and (3) the mutual parent concept’s distribution layer. Then, feature mapping is performed by assigning the concepts’ similarities to the concept features. Second, a hybrid genetic-whale optimization algorithm was proposed to optimize ontology learning from Arabic text. The operator of the G-WOA is a hybrid operator integrating GA’s mutation, crossover, and selection processes with the WOA’s processes (encircling prey, attacking of bubble-net, and searching for prey) to fulfill the balance between both exploitation and exploration, and to find the solutions that exhibit the highest fitness. For evaluating the performance of the ontology learning approach, extensive comparisons are conducted using different Arabic corpora and bio-inspired optimization algorithms. Furthermore, two publicly available non-Arabic corpora are used to compare the efficiency of the proposed approach with those of other languages. The results reveal that the proposed genetic-whale optimization algorithm outperforms the other compared algorithms across all the Arabic corpora in terms of precision, recall, and F-score measures. Moreover, the proposed approach outperforms the state-of-the-art methods of ontology learning from Arabic and non-Arabic texts in terms of these three measures.MDPI2025-05-13T13:35:35Z2025-05-13T13:35:35Z2019ArticleRania M. Ghoniem, Nawal Alhelwa and Khaled Shaalan (2019) “A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text,” Algorithms, 12(9), p. 182.https://bspace.buid.ac.ae/handle/1234/2988https://doi.org/10.3390/a12090182.enAlgorithmsv12 n9 (20190801): 182oai:bspace.buid.ac.ae:1234/29882025-05-13T13:40:26Z |
| spellingShingle | A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text M. Ghoniem , Rania text mining; ontology learning; hybrid models; genetic algorithms; whale optimization algorithm |
| title | A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text |
| title_full | A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text |
| title_fullStr | A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text |
| title_full_unstemmed | A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text |
| title_short | A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text |
| title_sort | A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text |
| topic | text mining; ontology learning; hybrid models; genetic algorithms; whale optimization algorithm |
| url | https://bspace.buid.ac.ae/handle/1234/2988 https://doi.org/10.3390/a12090182. |