---
_id: '60990'
abstract:
- lang: eng
  text: 'Large Language Models (LLMs) have demonstrated remarkable performance across
    a wide range of natural language processing tasks. However, their effectiveness
    in low-resource languages remains underexplored, particularly in complex tasks
    such as end-to-end Entity Linking (EL), which requires both mention detection
    and disambiguation against a knowledge base (KB). In earlier work, we introduced
    IndEL — the first end-to-end EL benchmark dataset for the Indonesian language
    — covering both a general domain (news) and a specific domain (religious text
    from the Indonesian translation of the Quran), and evaluated four traditional
    end-to-end EL systems on this dataset. In this study, we propose ELEVATE-ID, a
    comprehensive evaluation framework for assessing LLM performance on end-to-end
    EL in Indonesian. The framework evaluates LLMs under both zero-shot and fine-tuned
    conditions, using multilingual and Indonesian monolingual models, with Wikidata
    as the target KB. Our experiments include performance benchmarking, generalization
    analysis across domains, and systematic error analysis. Results show that GPT-4
    and GPT-3.5 achieve the highest accuracy in zero-shot and fine-tuned settings,
    respectively. However, even fine-tuned GPT-3.5 underperforms compared to DBpedia
    Spotlight — the weakest of the traditional model baselines — in the general domain.
    Interestingly, GPT-3.5 outperforms Babelfy in the specific domain. Generalization
    analysis indicates that fine-tuned GPT-3.5 adapts more effectively to cross-domain
    and mixed-domain scenarios. Error analysis uncovers persistent challenges that
    hinder LLM performance: difficulties with non-complete mentions, acronym disambiguation,
    and full-name recognition in formal contexts. These issues point to limitations
    in mention boundary detection and contextual grounding. Indonesian-pretrained
    LLMs, Komodo and Merak, reveal core weaknesses: template leakage and entity hallucination,
    respectively—underscoring architectural and training limitations in low-resource
    end-to-end EL.11Code and dataset are available at https://github.com/dice-group/ELEVATE-ID.'
article_type: original
author:
- first_name: Ria Hari
  full_name: Gusmita, Ria Hari
  id: '71039'
  last_name: Gusmita
- first_name: Asep Fajar
  full_name: Firmansyah, Asep Fajar
  id: '76787'
  last_name: Firmansyah
- first_name: Hamada Mohamed Abdelsamee
  full_name: Zahera, Hamada Mohamed Abdelsamee
  id: '72768'
  last_name: Zahera
  orcid: 0000-0003-0215-1278
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
citation:
  ama: 'Gusmita RH, Firmansyah AF, Zahera HMA, Ngonga Ngomo A-C. ELEVATE-ID: Extending
    Large Language Models for End-to-End Entity Linking Evaluation in Indonesian.
    <i>Data &#38; Knowledge Engineering</i>. 2026;161:102504. doi:<a href="https://doi.org/10.1016/j.datak.2025.102504">https://doi.org/10.1016/j.datak.2025.102504</a>'
  apa: 'Gusmita, R. H., Firmansyah, A. F., Zahera, H. M. A., &#38; Ngonga Ngomo, A.-C.
    (2026). ELEVATE-ID: Extending Large Language Models for End-to-End Entity Linking
    Evaluation in Indonesian. <i>Data &#38; Knowledge Engineering</i>, <i>161</i>,
    102504. <a href="https://doi.org/10.1016/j.datak.2025.102504">https://doi.org/10.1016/j.datak.2025.102504</a>'
  bibtex: '@article{Gusmita_Firmansyah_Zahera_Ngonga Ngomo_2026, title={ELEVATE-ID:
    Extending Large Language Models for End-to-End Entity Linking Evaluation in Indonesian},
    volume={161}, DOI={<a href="https://doi.org/10.1016/j.datak.2025.102504">https://doi.org/10.1016/j.datak.2025.102504</a>},
    journal={Data &#38; Knowledge Engineering}, author={Gusmita, Ria Hari and Firmansyah,
    Asep Fajar and Zahera, Hamada Mohamed Abdelsamee and Ngonga Ngomo, Axel-Cyrille},
    year={2026}, pages={102504} }'
  chicago: 'Gusmita, Ria Hari, Asep Fajar Firmansyah, Hamada Mohamed Abdelsamee Zahera,
    and Axel-Cyrille Ngonga Ngomo. “ELEVATE-ID: Extending Large Language Models for
    End-to-End Entity Linking Evaluation in Indonesian.” <i>Data &#38; Knowledge Engineering</i>
    161 (2026): 102504. <a href="https://doi.org/10.1016/j.datak.2025.102504">https://doi.org/10.1016/j.datak.2025.102504</a>.'
  ieee: 'R. H. Gusmita, A. F. Firmansyah, H. M. A. Zahera, and A.-C. Ngonga Ngomo,
    “ELEVATE-ID: Extending Large Language Models for End-to-End Entity Linking Evaluation
    in Indonesian,” <i>Data &#38; Knowledge Engineering</i>, vol. 161, p. 102504,
    2026, doi: <a href="https://doi.org/10.1016/j.datak.2025.102504">https://doi.org/10.1016/j.datak.2025.102504</a>.'
  mla: 'Gusmita, Ria Hari, et al. “ELEVATE-ID: Extending Large Language Models for
    End-to-End Entity Linking Evaluation in Indonesian.” <i>Data &#38; Knowledge Engineering</i>,
    vol. 161, 2026, p. 102504, doi:<a href="https://doi.org/10.1016/j.datak.2025.102504">https://doi.org/10.1016/j.datak.2025.102504</a>.'
  short: R.H. Gusmita, A.F. Firmansyah, H.M.A. Zahera, A.-C. Ngonga Ngomo, Data &#38;
    Knowledge Engineering 161 (2026) 102504.
date_created: 2025-08-24T11:38:51Z
date_updated: 2025-08-25T09:40:13Z
department:
- _id: '574'
doi: https://doi.org/10.1016/j.datak.2025.102504
intvolume: '       161'
keyword:
- LLMs
- Evaluation
- End-to-end EL
- Indonesian
language:
- iso: eng
main_file_link:
- url: https://www.sciencedirect.com/science/article/pii/S0169023X25000990?utm_campaign=STMJ_220042_AUTH_SERV_PA&utm_medium=email&utm_acid=78351008&SIS_ID=&dgcid=STMJ_220042_AUTH_SERV_PA&CMX_ID=&utm_in=DM591673&utm_source=AC_
page: '102504'
publication: Data & Knowledge Engineering
publication_identifier:
  issn:
  - 0169-023X
status: public
title: 'ELEVATE-ID: Extending Large Language Models for End-to-End Entity Linking
  Evaluation in Indonesian'
type: journal_article
user_id: '71039'
volume: 161
year: '2026'
...
---
_id: '57324'
abstract:
- lang: eng
  text: Generating SPARQL queries is crucial for extracting relevant information from
    diverse knowledge graphs. However, the structural and semantic differences among
    these graphs necessitate training or fine-tuning a tailored model for each one.
    In this paper, we propose UniQ-Gen, a unified query generation approach to generate
    SPARQL queries across various knowledge graphs. UniQ-Gen integrates entity recognition,
    disambiguation, and linking through a BERT-NER model and employs cross-encoder
    ranking to align questions with the Freebase ontology. We conducted several experiments
    on different benchmark datasets such as LC-QuAD 2.0, GrailQA, and QALD-10. The
    evaluation results demonstrate that our approach achieves performance equivalent
    to or better than models fine-tuned for individual knowledge graphs. This finding
    suggests that fine-tuning a unified model on a heterogeneous dataset of SPARQL
    queries across different knowledge graphs eliminates the need for separate models
    for each graph, thereby reducing resource requirements.
author:
- first_name: Daniel
  full_name: Vollmers, Daniel
  last_name: Vollmers
- first_name: Nikit
  full_name: Srivastava, Nikit
  id: '70066'
  last_name: Srivastava
  orcid: 0009-0004-5164-4911
- first_name: Hamada Mohamed Abdelsamee
  full_name: Zahera, Hamada Mohamed Abdelsamee
  id: '72768'
  last_name: Zahera
  orcid: 0000-0003-0215-1278
- first_name: Diego
  full_name: Moussallem, Diego
  id: '71635'
  last_name: Moussallem
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
citation:
  ama: 'Vollmers D, Srivastava N, Zahera HMA, Moussallem D, Ngonga Ngomo A-C. UniQ-Gen:
    Unified Query Generation Across Multiple Knowledge Graphs. In: Alam M, Rospocher
    M, van Erp M, Hollink L, Gesese GA, eds. <i>Knowledge Engineering and Knowledge
    Management</i>. Springer Nature Switzerland; 2025:174–189. doi:<a href="https://doi.org/10.1007/978-3-031-77792-9_11">https://doi.org/10.1007/978-3-031-77792-9_11</a>'
  apa: 'Vollmers, D., Srivastava, N., Zahera, H. M. A., Moussallem, D., &#38; Ngonga
    Ngomo, A.-C. (2025). UniQ-Gen: Unified Query Generation Across Multiple Knowledge
    Graphs. In M. Alam, M. Rospocher, M. van Erp, L. Hollink, &#38; G. A. Gesese (Eds.),
    <i>Knowledge Engineering and Knowledge Management</i> (pp. 174–189). Springer
    Nature Switzerland. <a href="https://doi.org/10.1007/978-3-031-77792-9_11">https://doi.org/10.1007/978-3-031-77792-9_11</a>'
  bibtex: '@inproceedings{Vollmers_Srivastava_Zahera_Moussallem_Ngonga Ngomo_2025,
    place={Cham}, title={UniQ-Gen: Unified Query Generation Across Multiple Knowledge
    Graphs}, DOI={<a href="https://doi.org/10.1007/978-3-031-77792-9_11">https://doi.org/10.1007/978-3-031-77792-9_11</a>},
    booktitle={Knowledge Engineering and Knowledge Management}, publisher={Springer
    Nature Switzerland}, author={Vollmers, Daniel and Srivastava, Nikit and Zahera,
    Hamada Mohamed Abdelsamee and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille},
    editor={Alam, Mehwish and Rospocher, Marco and van Erp, Marieke and Hollink, Laura
    and Gesese, Genet Asefa}, year={2025}, pages={174–189} }'
  chicago: 'Vollmers, Daniel, Nikit Srivastava, Hamada Mohamed Abdelsamee Zahera,
    Diego Moussallem, and Axel-Cyrille Ngonga Ngomo. “UniQ-Gen: Unified Query Generation
    Across Multiple Knowledge Graphs.” In <i>Knowledge Engineering and Knowledge Management</i>,
    edited by Mehwish Alam, Marco Rospocher, Marieke van Erp, Laura Hollink, and Genet
    Asefa Gesese, 174–189. Cham: Springer Nature Switzerland, 2025. <a href="https://doi.org/10.1007/978-3-031-77792-9_11">https://doi.org/10.1007/978-3-031-77792-9_11</a>.'
  ieee: 'D. Vollmers, N. Srivastava, H. M. A. Zahera, D. Moussallem, and A.-C. Ngonga
    Ngomo, “UniQ-Gen: Unified Query Generation Across Multiple Knowledge Graphs,”
    in <i>Knowledge Engineering and Knowledge Management</i>, 2025, pp. 174–189, doi:
    <a href="https://doi.org/10.1007/978-3-031-77792-9_11">https://doi.org/10.1007/978-3-031-77792-9_11</a>.'
  mla: 'Vollmers, Daniel, et al. “UniQ-Gen: Unified Query Generation Across Multiple
    Knowledge Graphs.” <i>Knowledge Engineering and Knowledge Management</i>, edited
    by Mehwish Alam et al., Springer Nature Switzerland, 2025, pp. 174–189, doi:<a
    href="https://doi.org/10.1007/978-3-031-77792-9_11">https://doi.org/10.1007/978-3-031-77792-9_11</a>.'
  short: 'D. Vollmers, N. Srivastava, H.M.A. Zahera, D. Moussallem, A.-C. Ngonga Ngomo,
    in: M. Alam, M. Rospocher, M. van Erp, L. Hollink, G.A. Gesese (Eds.), Knowledge
    Engineering and Knowledge Management, Springer Nature Switzerland, Cham, 2025,
    pp. 174–189.'
date_created: 2024-11-22T12:56:54Z
date_updated: 2024-11-22T12:58:59Z
doi: https://doi.org/10.1007/978-3-031-77792-9_11
editor:
- first_name: Mehwish
  full_name: Alam, Mehwish
  last_name: Alam
- first_name: Marco
  full_name: Rospocher, Marco
  last_name: Rospocher
- first_name: Marieke
  full_name: van Erp, Marieke
  last_name: van Erp
- first_name: Laura
  full_name: Hollink, Laura
  last_name: Hollink
- first_name: Genet Asefa
  full_name: Gesese, Genet Asefa
  last_name: Gesese
language:
- iso: eng
page: 174–189
place: Cham
publication: Knowledge Engineering and Knowledge Management
publication_identifier:
  isbn:
  - 978-3-031-77792-9
publisher: Springer Nature Switzerland
status: public
title: 'UniQ-Gen: Unified Query Generation Across Multiple Knowledge Graphs'
type: conference
user_id: '70066'
year: '2025'
...
---
_id: '59054'
author:
- first_name: Asep Fajar
  full_name: Firmansyah, Asep Fajar
  id: '76787'
  last_name: Firmansyah
- first_name: Hamada Mohamed Abdelsamee
  full_name: Zahera, Hamada Mohamed Abdelsamee
  id: '72768'
  last_name: Zahera
  orcid: 0000-0003-0215-1278
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Diego
  full_name: Moussallem, Diego
  id: '71635'
  last_name: Moussallem
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
citation:
  ama: 'Firmansyah AF, Zahera HMA, Sherif M, Moussallem D, Ngonga Ngomo A-C. ANTS:
    Abstractive Entity Summarization in Knowledge Graphs. In: <i>ESWC2025</i>. The
    Semantic Web. pringer Nature Switzerland; 2025:133--151. doi:<a href="https://doi.org/10.1007/978-3-031-94575-5_8">10.1007/978-3-031-94575-5_8</a>'
  apa: 'Firmansyah, A. F., Zahera, H. M. A., Sherif, M., Moussallem, D., &#38; Ngonga
    Ngomo, A.-C. (2025). ANTS: Abstractive Entity Summarization in Knowledge Graphs.
    <i>ESWC2025</i>, 133--151. <a href="https://doi.org/10.1007/978-3-031-94575-5_8">https://doi.org/10.1007/978-3-031-94575-5_8</a>'
  bibtex: '@inproceedings{Firmansyah_Zahera_Sherif_Moussallem_Ngonga Ngomo_2025, series={The
    Semantic Web}, title={ANTS: Abstractive Entity Summarization in Knowledge Graphs},
    DOI={<a href="https://doi.org/10.1007/978-3-031-94575-5_8">10.1007/978-3-031-94575-5_8</a>},
    booktitle={ESWC2025}, publisher={pringer Nature Switzerland}, author={Firmansyah,
    Asep Fajar and Zahera, Hamada Mohamed Abdelsamee and Sherif, Mohamed and Moussallem,
    Diego and Ngonga Ngomo, Axel-Cyrille}, year={2025}, pages={133--151}, collection={The
    Semantic Web} }'
  chicago: 'Firmansyah, Asep Fajar, Hamada Mohamed Abdelsamee Zahera, Mohamed Sherif,
    Diego Moussallem, and Axel-Cyrille Ngonga Ngomo. “ANTS: Abstractive Entity Summarization
    in Knowledge Graphs.” In <i>ESWC2025</i>, 133--151. The Semantic Web. pringer
    Nature Switzerland, 2025. <a href="https://doi.org/10.1007/978-3-031-94575-5_8">https://doi.org/10.1007/978-3-031-94575-5_8</a>.'
  ieee: 'A. F. Firmansyah, H. M. A. Zahera, M. Sherif, D. Moussallem, and A.-C. Ngonga
    Ngomo, “ANTS: Abstractive Entity Summarization in Knowledge Graphs,” in <i>ESWC2025</i>,
    2025, pp. 133--151, doi: <a href="https://doi.org/10.1007/978-3-031-94575-5_8">10.1007/978-3-031-94575-5_8</a>.'
  mla: 'Firmansyah, Asep Fajar, et al. “ANTS: Abstractive Entity Summarization in
    Knowledge Graphs.” <i>ESWC2025</i>, pringer Nature Switzerland, 2025, pp. 133--151,
    doi:<a href="https://doi.org/10.1007/978-3-031-94575-5_8">10.1007/978-3-031-94575-5_8</a>.'
  short: 'A.F. Firmansyah, H.M.A. Zahera, M. Sherif, D. Moussallem, A.-C. Ngonga Ngomo,
    in: ESWC2025, pringer Nature Switzerland, 2025, pp. 133--151.'
conference:
  name: ESWC 2025
date_created: 2025-03-17T13:41:48Z
date_updated: 2025-09-11T10:00:44Z
department:
- _id: '574'
doi: 10.1007/978-3-031-94575-5_8
keyword:
- firmansyah mousallem ngonga sherif zahera
language:
- iso: eng
page: 133--151
publication: ESWC2025
publication_identifier:
  isbn:
  - 978-3-031-94575-5
publication_status: published
publisher: pringer Nature Switzerland
series_title: The Semantic Web
status: public
title: 'ANTS: Abstractive Entity Summarization in Knowledge Graphs'
type: conference
user_id: '67234'
year: '2025'
...
---
_id: '61134'
author:
- first_name: Ali
  full_name: Manzoor, Ali
  id: '77309'
  last_name: Manzoor
  orcid: https://orcid.org/0000-0001-8403-5160
- first_name: René
  full_name: Speck, René
  id: '70843'
  last_name: Speck
- first_name: Hamada Mohamed Abdelsamee
  full_name: Zahera, Hamada Mohamed Abdelsamee
  id: '72768'
  last_name: Zahera
  orcid: 0000-0003-0215-1278
- first_name: Muhammad
  full_name: Saleem, Muhammad
  last_name: Saleem
- first_name: Diego
  full_name: Moussallem, Diego
  id: '71635'
  last_name: Moussallem
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
citation:
  ama: Manzoor A, Speck R, Zahera HMA, Saleem M, Moussallem D, Ngonga Ngomo A-C. Multilingual
    Relation Extraction - A Survey. <i>IEEE Access</i>. Published online 2025:1-1.
    doi:<a href="https://doi.org/10.1109/access.2025.3604258">10.1109/access.2025.3604258</a>
  apa: Manzoor, A., Speck, R., Zahera, H. M. A., Saleem, M., Moussallem, D., &#38;
    Ngonga Ngomo, A.-C. (2025). Multilingual Relation Extraction - A Survey. <i>IEEE
    Access</i>, 1–1. <a href="https://doi.org/10.1109/access.2025.3604258">https://doi.org/10.1109/access.2025.3604258</a>
  bibtex: '@article{Manzoor_Speck_Zahera_Saleem_Moussallem_Ngonga Ngomo_2025, title={Multilingual
    Relation Extraction - A Survey}, DOI={<a href="https://doi.org/10.1109/access.2025.3604258">10.1109/access.2025.3604258</a>},
    journal={IEEE Access}, publisher={Institute of Electrical and Electronics Engineers
    (IEEE)}, author={Manzoor, Ali and Speck, René and Zahera, Hamada Mohamed Abdelsamee
    and Saleem, Muhammad and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}, year={2025},
    pages={1–1} }'
  chicago: Manzoor, Ali, René Speck, Hamada Mohamed Abdelsamee Zahera, Muhammad Saleem,
    Diego Moussallem, and Axel-Cyrille Ngonga Ngomo. “Multilingual Relation Extraction
    - A Survey.” <i>IEEE Access</i>, 2025, 1–1. <a href="https://doi.org/10.1109/access.2025.3604258">https://doi.org/10.1109/access.2025.3604258</a>.
  ieee: 'A. Manzoor, R. Speck, H. M. A. Zahera, M. Saleem, D. Moussallem, and A.-C.
    Ngonga Ngomo, “Multilingual Relation Extraction - A Survey,” <i>IEEE Access</i>,
    pp. 1–1, 2025, doi: <a href="https://doi.org/10.1109/access.2025.3604258">10.1109/access.2025.3604258</a>.'
  mla: Manzoor, Ali, et al. “Multilingual Relation Extraction - A Survey.” <i>IEEE
    Access</i>, Institute of Electrical and Electronics Engineers (IEEE), 2025, pp.
    1–1, doi:<a href="https://doi.org/10.1109/access.2025.3604258">10.1109/access.2025.3604258</a>.
  short: A. Manzoor, R. Speck, H.M.A. Zahera, M. Saleem, D. Moussallem, A.-C. Ngonga
    Ngomo, IEEE Access (2025) 1–1.
date_created: 2025-09-04T13:32:08Z
date_updated: 2025-09-11T11:14:27Z
department:
- _id: '574'
doi: 10.1109/access.2025.3604258
language:
- iso: eng
page: 1-1
publication: IEEE Access
publication_identifier:
  issn:
  - 2169-3536
publication_status: published
publisher: Institute of Electrical and Electronics Engineers (IEEE)
status: public
title: Multilingual Relation Extraction - A Survey
type: journal_article
user_id: '77309'
year: '2025'
...
---
_id: '61041'
abstract:
- lang: eng
  text: Large Language Models (LLMs) are increasingly deployed in real-world applications
    that require access to up-to-date knowledge. However, retraining LLMs is computationally
    expensive. Therefore, knowledge editing techniques are crucial for maintaining
    current information and correcting erroneous assertions within pre-trained models.
    Current benchmarks for knowledge editing primarily focus on recalling edited facts,
    often neglecting their logical consequences. To address this limitation, we introduce
    a new benchmark designed to evaluate how knowledge editing methods handle the
    logical consequences of a single fact edit. Our benchmark extracts relevant logical
    rules from a knowledge graph for a given edit. Then, it generates multi-hop questions
    based on these rules to assess the impact on logical consequences. Our findings
    indicate that while existing knowledge editing approaches can accurately insert
    direct assertions into LLMs, they frequently fail to inject entailed knowledge.
    Specifically, experiments with popular methods like ROME and FT reveal a substantial
    performance gap, up to 24%, between evaluations on directly edited knowledge and
    on entailed knowledge. This highlights the critical need for semantics-aware evaluation
    frameworks in knowledge editing.
author:
- first_name: Tatiana
  full_name: Moteu Ngoli, Tatiana
  id: '99174'
  last_name: Moteu Ngoli
- first_name: N'Dah Jean
  full_name: Kouagou, N'Dah Jean
  id: '87189'
  last_name: Kouagou
- first_name: Hamada Mohamed Abdelsamee
  full_name: Zahera, Hamada Mohamed Abdelsamee
  id: '72768'
  last_name: Zahera
  orcid: 0000-0003-0215-1278
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
citation:
  ama: 'Moteu Ngoli T, Kouagou NJ, Zahera HMA, Ngonga Ngomo A-C. Benchmarking Knowledge
    Editing using Logical Rules. In: <i>Proceedings of the 24th International Semantic
    Web Conference (ISWC 2025)</i>. Springer, Cham; 2025:pp 41-56. doi:<a href="https://doi.org/10.1007/978-3-032-09530-5_3">https://doi.org/10.1007/978-3-032-09530-5_3</a>'
  apa: Moteu Ngoli, T., Kouagou, N. J., Zahera, H. M. A., &#38; Ngonga Ngomo, A.-C.
    (2025). Benchmarking Knowledge Editing using Logical Rules. <i>Proceedings of
    the 24th International Semantic Web Conference (ISWC 2025)</i>, pp 41-56. <a href="https://doi.org/10.1007/978-3-032-09530-5_3">https://doi.org/10.1007/978-3-032-09530-5_3</a>
  bibtex: '@inproceedings{Moteu Ngoli_Kouagou_Zahera_Ngonga Ngomo_2025, title={Benchmarking
    Knowledge Editing using Logical Rules}, DOI={<a href="https://doi.org/10.1007/978-3-032-09530-5_3">https://doi.org/10.1007/978-3-032-09530-5_3</a>},
    booktitle={Proceedings of the 24th International Semantic Web Conference (ISWC
    2025)}, publisher={Springer, Cham}, author={Moteu Ngoli, Tatiana and Kouagou,
    N’Dah Jean and Zahera, Hamada Mohamed Abdelsamee and Ngonga Ngomo, Axel-Cyrille},
    year={2025}, pages={pp 41-56} }'
  chicago: Moteu Ngoli, Tatiana, N’Dah Jean Kouagou, Hamada Mohamed Abdelsamee Zahera,
    and Axel-Cyrille Ngonga Ngomo. “Benchmarking Knowledge Editing Using Logical Rules.”
    In <i>Proceedings of the 24th International Semantic Web Conference (ISWC 2025)</i>,
    pp 41-56. Springer, Cham, 2025. <a href="https://doi.org/10.1007/978-3-032-09530-5_3">https://doi.org/10.1007/978-3-032-09530-5_3</a>.
  ieee: 'T. Moteu Ngoli, N. J. Kouagou, H. M. A. Zahera, and A.-C. Ngonga Ngomo, “Benchmarking
    Knowledge Editing using Logical Rules,” in <i>Proceedings of the 24th International
    Semantic Web Conference (ISWC 2025)</i>, Nara, Japan, 2025, p. pp 41-56, doi:
    <a href="https://doi.org/10.1007/978-3-032-09530-5_3">https://doi.org/10.1007/978-3-032-09530-5_3</a>.'
  mla: Moteu Ngoli, Tatiana, et al. “Benchmarking Knowledge Editing Using Logical
    Rules.” <i>Proceedings of the 24th International Semantic Web Conference (ISWC
    2025)</i>, Springer, Cham, 2025, p. pp 41-56, doi:<a href="https://doi.org/10.1007/978-3-032-09530-5_3">https://doi.org/10.1007/978-3-032-09530-5_3</a>.
  short: 'T. Moteu Ngoli, N.J. Kouagou, H.M.A. Zahera, A.-C. Ngonga Ngomo, in: Proceedings
    of the 24th International Semantic Web Conference (ISWC 2025), Springer, Cham,
    2025, p. pp 41-56.'
conference:
  end_date: 2025.11.6
  location: Nara, Japan
  name: The 24th International Semantic Web Conference (ISWC 2025)
  start_date: 2025.11.2
date_created: 2025-08-27T13:17:55Z
date_updated: 2025-12-01T10:04:25Z
department:
- _id: '574'
doi: https://doi.org/10.1007/978-3-032-09530-5_3
keyword:
- dice sailproject moteu kouagou zahera ngonga
language:
- iso: eng
page: pp 41-56
project:
- _id: '285'
  name: SAIL - Nachhaltiger Lebenszyklus von intelligenten soziotechnischen Systemen
publication: Proceedings of the 24th International Semantic Web Conference (ISWC 2025)
publication_identifier:
  isbn:
  - 978-3-032-09530-5
publication_status: published
publisher: Springer, Cham
status: public
title: Benchmarking Knowledge Editing using Logical Rules
type: conference
user_id: '99174'
year: '2025'
...
---
_id: '61753'
abstract:
- lang: eng
  text: This paper presents LOLA, a massively multilingual large language model trained
    on more than 160 languages using a sparse Mixture-of-Experts Transformer architecture.
    Our architectural and implementation choices address the challenge of harnessing
    linguistic diversity while maintaining efficiency and avoiding the common pitfalls
    of multilinguality. Our analysis of the evaluation results shows competitive performance
    in natural language generation and understanding tasks. Additionally, we demonstrate
    how the learned expert-routing mechanism exploits implicit phylogenetic linguistic
    patterns to potentially alleviate the curse of multilinguality. We provide an
    in-depth look at the training process, an analysis of the datasets, and a balanced
    exploration of the model{’}s strengths and limitations. As an open-source model,
    LOLA promotes reproducibility and serves as a robust foundation for future research.
    Our findings enable the development of compute-efficient multilingual models with
    strong, scalable performance across languages.
author:
- first_name: Nikit
  full_name: Srivastava, Nikit
  id: '70066'
  last_name: Srivastava
  orcid: 0009-0004-5164-4911
- first_name: Denis
  full_name: Kuchelev, Denis
  id: '70842'
  last_name: Kuchelev
- first_name: Tatiana
  full_name: Moteu Ngoli, Tatiana
  id: '99174'
  last_name: Moteu Ngoli
- first_name: Kshitij
  full_name: Shetty, Kshitij
  last_name: Shetty
- first_name: Michael
  full_name: Röder, Michael
  id: '67199'
  last_name: Röder
  orcid: https://orcid.org/0000-0002-8609-8277
- first_name: Hamada Mohamed Abdelsamee
  full_name: Zahera, Hamada Mohamed Abdelsamee
  id: '72768'
  last_name: Zahera
  orcid: 0000-0003-0215-1278
- first_name: Diego
  full_name: Moussallem, Diego
  id: '71635'
  last_name: Moussallem
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
citation:
  ama: 'Srivastava N, Kuchelev D, Moteu Ngoli T, et al. LOLA – An Open-Source Massively
    Multilingual Large Language Model. In: Rambow O, Wanner L, Apidianaki M, Al-Khalifa
    H, Eugenio BD, Schockaert S, eds. <i>Proceedings of the 31st International Conference
    on Computational Linguistics</i>. Association for Computational Linguistics; 2025:6420–6446.'
  apa: Srivastava, N., Kuchelev, D., Moteu Ngoli, T., Shetty, K., Röder, M., Zahera,
    H. M. A., Moussallem, D., &#38; Ngonga Ngomo, A.-C. (2025). LOLA – An Open-Source
    Massively Multilingual Large Language Model. In O. Rambow, L. Wanner, M. Apidianaki,
    H. Al-Khalifa, B. D. Eugenio, &#38; S. Schockaert (Eds.), <i>Proceedings of the
    31st International Conference on Computational Linguistics</i> (pp. 6420–6446).
    Association for Computational Linguistics.
  bibtex: '@inproceedings{Srivastava_Kuchelev_Moteu Ngoli_Shetty_Röder_Zahera_Moussallem_Ngonga
    Ngomo_2025, place={Abu Dhabi, UAE}, title={LOLA – An Open-Source Massively Multilingual
    Large Language Model}, booktitle={Proceedings of the 31st International Conference
    on Computational Linguistics}, publisher={Association for Computational Linguistics},
    author={Srivastava, Nikit and Kuchelev, Denis and Moteu Ngoli, Tatiana and Shetty,
    Kshitij and Röder, Michael and Zahera, Hamada Mohamed Abdelsamee and Moussallem,
    Diego and Ngonga Ngomo, Axel-Cyrille}, editor={Rambow, Owen and Wanner, Leo and
    Apidianaki, Marianna and Al-Khalifa, Hend and Eugenio, Barbara Di and Schockaert,
    Steven}, year={2025}, pages={6420–6446} }'
  chicago: 'Srivastava, Nikit, Denis Kuchelev, Tatiana Moteu Ngoli, Kshitij Shetty,
    Michael Röder, Hamada Mohamed Abdelsamee Zahera, Diego Moussallem, and Axel-Cyrille
    Ngonga Ngomo. “LOLA – An Open-Source Massively Multilingual Large Language Model.”
    In <i>Proceedings of the 31st International Conference on Computational Linguistics</i>,
    edited by Owen Rambow, Leo Wanner, Marianna Apidianaki, Hend Al-Khalifa, Barbara
    Di Eugenio, and Steven Schockaert, 6420–6446. Abu Dhabi, UAE: Association for
    Computational Linguistics, 2025.'
  ieee: N. Srivastava <i>et al.</i>, “LOLA – An Open-Source Massively Multilingual
    Large Language Model,” in <i>Proceedings of the 31st International Conference
    on Computational Linguistics</i>, 2025, pp. 6420–6446.
  mla: Srivastava, Nikit, et al. “LOLA – An Open-Source Massively Multilingual Large
    Language Model.” <i>Proceedings of the 31st International Conference on Computational
    Linguistics</i>, edited by Owen Rambow et al., Association for Computational Linguistics,
    2025, pp. 6420–6446.
  short: 'N. Srivastava, D. Kuchelev, T. Moteu Ngoli, K. Shetty, M. Röder, H.M.A.
    Zahera, D. Moussallem, A.-C. Ngonga Ngomo, in: O. Rambow, L. Wanner, M. Apidianaki,
    H. Al-Khalifa, B.D. Eugenio, S. Schockaert (Eds.), Proceedings of the 31st International
    Conference on Computational Linguistics, Association for Computational Linguistics,
    Abu Dhabi, UAE, 2025, pp. 6420–6446.'
date_created: 2025-10-08T11:02:30Z
date_updated: 2026-01-06T10:11:37Z
editor:
- first_name: Owen
  full_name: Rambow, Owen
  last_name: Rambow
- first_name: Leo
  full_name: Wanner, Leo
  last_name: Wanner
- first_name: Marianna
  full_name: Apidianaki, Marianna
  last_name: Apidianaki
- first_name: Hend
  full_name: Al-Khalifa, Hend
  last_name: Al-Khalifa
- first_name: Barbara Di
  full_name: Eugenio, Barbara Di
  last_name: Eugenio
- first_name: Steven
  full_name: Schockaert, Steven
  last_name: Schockaert
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://aclanthology.org/2025.coling-main.428.pdf
oa: '1'
page: 6420–6446
place: Abu Dhabi, UAE
publication: Proceedings of the 31st International Conference on Computational Linguistics
publisher: Association for Computational Linguistics
status: public
title: LOLA – An Open-Source Massively Multilingual Large Language Model
type: conference
user_id: '70066'
year: '2025'
...
---
_id: '54449'
author:
- first_name: N'Dah Jean
  full_name: KOUAGOU, N'Dah Jean
  id: '87189'
  last_name: KOUAGOU
- first_name: Caglar
  full_name: Demir, Caglar
  id: '43817'
  last_name: Demir
- first_name: Hamada Mohamed Abdelsamee
  full_name: Zahera, Hamada Mohamed Abdelsamee
  id: '72768'
  last_name: Zahera
  orcid: 0000-0003-0215-1278
- first_name: Adrian
  full_name: Wilke, Adrian
  id: '9101'
  last_name: Wilke
  orcid: 0000-0002-6575-807X
- first_name: Stefan
  full_name: Heindorf, Stefan
  id: '11871'
  last_name: Heindorf
  orcid: 0000-0002-4525-6865
- first_name: Jiayi
  full_name: Li, Jiayi
  last_name: Li
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
citation:
  ama: 'KOUAGOU NJ, Demir C, Zahera HMA, et al. Universal Knowledge Graph Embeddings.
    In: <i>Companion Proceedings of the ACM on Web Conference 2024</i>. ACM; 2024.
    doi:<a href="https://doi.org/10.1145/3589335.3651978">10.1145/3589335.3651978</a>'
  apa: KOUAGOU, N. J., Demir, C., Zahera, H. M. A., Wilke, A., Heindorf, S., Li, J.,
    &#38; Ngonga Ngomo, A.-C. (2024). Universal Knowledge Graph Embeddings. <i>Companion
    Proceedings of the ACM on Web Conference 2024</i>. Companion Proceedings of the
    ACM on Web Conference 2024, Singapore. <a href="https://doi.org/10.1145/3589335.3651978">https://doi.org/10.1145/3589335.3651978</a>
  bibtex: '@inproceedings{KOUAGOU_Demir_Zahera_Wilke_Heindorf_Li_Ngonga Ngomo_2024,
    title={Universal Knowledge Graph Embeddings}, DOI={<a href="https://doi.org/10.1145/3589335.3651978">10.1145/3589335.3651978</a>},
    booktitle={Companion Proceedings of the ACM on Web Conference 2024}, publisher={ACM},
    author={KOUAGOU, N’Dah Jean and Demir, Caglar and Zahera, Hamada Mohamed Abdelsamee
    and Wilke, Adrian and Heindorf, Stefan and Li, Jiayi and Ngonga Ngomo, Axel-Cyrille},
    year={2024} }'
  chicago: KOUAGOU, N’Dah Jean, Caglar Demir, Hamada Mohamed Abdelsamee Zahera, Adrian
    Wilke, Stefan Heindorf, Jiayi Li, and Axel-Cyrille Ngonga Ngomo. “Universal Knowledge
    Graph Embeddings.” In <i>Companion Proceedings of the ACM on Web Conference 2024</i>.
    ACM, 2024. <a href="https://doi.org/10.1145/3589335.3651978">https://doi.org/10.1145/3589335.3651978</a>.
  ieee: 'N. J. KOUAGOU <i>et al.</i>, “Universal Knowledge Graph Embeddings,” presented
    at the Companion Proceedings of the ACM on Web Conference 2024, Singapore, 2024,
    doi: <a href="https://doi.org/10.1145/3589335.3651978">10.1145/3589335.3651978</a>.'
  mla: KOUAGOU, N’Dah Jean, et al. “Universal Knowledge Graph Embeddings.” <i>Companion
    Proceedings of the ACM on Web Conference 2024</i>, ACM, 2024, doi:<a href="https://doi.org/10.1145/3589335.3651978">10.1145/3589335.3651978</a>.
  short: 'N.J. KOUAGOU, C. Demir, H.M.A. Zahera, A. Wilke, S. Heindorf, J. Li, A.-C.
    Ngonga Ngomo, in: Companion Proceedings of the ACM on Web Conference 2024, ACM,
    2024.'
conference:
  end_date: 2024-05-17
  location: Singapore
  name: Companion Proceedings of the ACM on Web Conference 2024
  start_date: 2024-05-13
date_created: 2024-05-26T18:52:47Z
date_updated: 2024-05-26T19:06:10Z
department:
- _id: '760'
- _id: '574'
doi: 10.1145/3589335.3651978
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://dl.acm.org/doi/abs/10.1145/3589335.3651978
oa: '1'
publication: Companion Proceedings of the ACM on Web Conference 2024
publication_status: published
publisher: ACM
status: public
title: Universal Knowledge Graph Embeddings
type: conference
user_id: '11871'
year: '2024'
...
---
_id: '57277'
author:
- first_name: Nikit
  full_name: Srivastava, Nikit
  id: '70066'
  last_name: Srivastava
  orcid: 0009-0004-5164-4911
- first_name: Mengshi
  full_name: Ma, Mengshi
  last_name: Ma
- first_name: Daniel
  full_name: Vollmers, Daniel
  last_name: Vollmers
- first_name: Hamada Mohamed Abdelsamee
  full_name: Zahera, Hamada Mohamed Abdelsamee
  id: '72768'
  last_name: Zahera
  orcid: 0000-0003-0215-1278
- first_name: Diego
  full_name: Moussallem, Diego
  id: '71635'
  last_name: Moussallem
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
citation:
  ama: Srivastava N, Ma M, Vollmers D, Zahera HMA, Moussallem D, Ngonga Ngomo A-C.
    MST5 -- Multilingual Question Answering over Knowledge Graphs. Published online
    2024.
  apa: Srivastava, N., Ma, M., Vollmers, D., Zahera, H. M. A., Moussallem, D., &#38;
    Ngonga Ngomo, A.-C. (2024). <i>MST5 -- Multilingual Question Answering over Knowledge
    Graphs</i>.
  bibtex: '@article{Srivastava_Ma_Vollmers_Zahera_Moussallem_Ngonga Ngomo_2024, title={MST5
    -- Multilingual Question Answering over Knowledge Graphs}, author={Srivastava,
    Nikit and Ma, Mengshi and Vollmers, Daniel and Zahera, Hamada Mohamed Abdelsamee
    and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}, year={2024} }'
  chicago: Srivastava, Nikit, Mengshi Ma, Daniel Vollmers, Hamada Mohamed Abdelsamee
    Zahera, Diego Moussallem, and Axel-Cyrille Ngonga Ngomo. “MST5 -- Multilingual
    Question Answering over Knowledge Graphs,” 2024.
  ieee: N. Srivastava, M. Ma, D. Vollmers, H. M. A. Zahera, D. Moussallem, and A.-C.
    Ngonga Ngomo, “MST5 -- Multilingual Question Answering over Knowledge Graphs.”
    2024.
  mla: Srivastava, Nikit, et al. <i>MST5 -- Multilingual Question Answering over Knowledge
    Graphs</i>. 2024.
  short: N. Srivastava, M. Ma, D. Vollmers, H.M.A. Zahera, D. Moussallem, A.-C. Ngonga
    Ngomo, (2024).
date_created: 2024-11-20T10:40:50Z
date_updated: 2024-11-20T10:57:42Z
language:
- iso: eng
status: public
title: MST5 -- Multilingual Question Answering over Knowledge Graphs
type: preprint
user_id: '70066'
year: '2024'
...
---
_id: '55094'
author:
- first_name: Hamada Mohamed Abdelsamee
  full_name: Zahera, Hamada Mohamed Abdelsamee
  id: '72768'
  last_name: Zahera
  orcid: 0000-0003-0215-1278
- first_name: Ali
  full_name: Manzoor, Ali
  id: '77309'
  last_name: Manzoor
  orcid: https://orcid.org/0000-0001-8403-5160
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Diego
  full_name: Moussallem, Diego
  id: '71635'
  last_name: Moussallem
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
citation:
  ama: 'Zahera HMA, Manzoor A, Sherif M, Moussallem D, Ngonga Ngomo A-C. Generating
    SPARQL from Natural Language Using Chain-of-Thoughts Prompting. In: <i>SEMANTiCS</i>.
    ; 2024.'
  apa: Zahera, H. M. A., Manzoor, A., Sherif, M., Moussallem, D., &#38; Ngonga Ngomo,
    A.-C. (2024). Generating SPARQL from Natural Language Using Chain-of-Thoughts
    Prompting. <i>SEMANTiCS</i>.
  bibtex: '@inproceedings{Zahera_Manzoor_Sherif_Moussallem_Ngonga Ngomo_2024, place={Amsterdam,
    Netherlands}, title={Generating SPARQL from Natural Language Using Chain-of-Thoughts
    Prompting}, booktitle={SEMANTiCS}, author={Zahera, Hamada Mohamed Abdelsamee and
    Manzoor, Ali and Sherif, Mohamed and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille},
    year={2024} }'
  chicago: Zahera, Hamada Mohamed Abdelsamee, Ali Manzoor, Mohamed Sherif, Diego Moussallem,
    and Axel-Cyrille Ngonga Ngomo. “Generating SPARQL from Natural Language Using
    Chain-of-Thoughts Prompting.” In <i>SEMANTiCS</i>. Amsterdam, Netherlands, 2024.
  ieee: H. M. A. Zahera, A. Manzoor, M. Sherif, D. Moussallem, and A.-C. Ngonga Ngomo,
    “Generating SPARQL from Natural Language Using Chain-of-Thoughts Prompting,” 2024.
  mla: Zahera, Hamada Mohamed Abdelsamee, et al. “Generating SPARQL from Natural Language
    Using Chain-of-Thoughts Prompting.” <i>SEMANTiCS</i>, 2024.
  short: 'H.M.A. Zahera, A. Manzoor, M. Sherif, D. Moussallem, A.-C. Ngonga Ngomo,
    in: SEMANTiCS, Amsterdam, Netherlands, 2024.'
date_created: 2024-07-05T13:59:24Z
date_updated: 2025-09-11T10:37:39Z
department:
- _id: '574'
keyword:
- TRR318 climatebowl colide dice enexa kiam manzoor moussallem ngonga sailproject
  sherif simba zahera
language:
- iso: eng
place: Amsterdam, Netherlands
publication: SEMANTiCS
status: public
title: Generating SPARQL from Natural Language Using Chain-of-Thoughts Prompting
type: conference
user_id: '67234'
year: '2024'
...
---
_id: '54608'
author:
- first_name: Hamada Mohamed Abdelsamee
  full_name: Zahera, Hamada Mohamed Abdelsamee
  id: '72768'
  last_name: Zahera
  orcid: 0000-0003-0215-1278
- first_name: Fedor
  full_name: Vitiugin, Fedor
  last_name: Vitiugin
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Carlos
  full_name: Castillo, Carlos
  last_name: Castillo
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
citation:
  ama: 'Zahera HMA, Vitiugin F, Sherif M, Castillo C, Ngonga Ngomo A-C. Using Pre-trained
    Language Models for Abstractive DBPEDIA Summarization: A Comparative Study. In:
    <i>SEMANTiCS</i>. ; 2023.'
  apa: 'Zahera, H. M. A., Vitiugin, F., Sherif, M., Castillo, C., &#38; Ngonga Ngomo,
    A.-C. (2023). Using Pre-trained Language Models for Abstractive DBPEDIA Summarization:
    A Comparative Study. <i>SEMANTiCS</i>.'
  bibtex: '@inproceedings{Zahera_Vitiugin_Sherif_Castillo_Ngonga Ngomo_2023, title={Using
    Pre-trained Language Models for Abstractive DBPEDIA Summarization: A Comparative
    Study}, booktitle={SEMANTiCS}, author={Zahera, Hamada Mohamed Abdelsamee and Vitiugin,
    Fedor and Sherif, Mohamed and Castillo, Carlos and Ngonga Ngomo, Axel-Cyrille},
    year={2023} }'
  chicago: 'Zahera, Hamada Mohamed Abdelsamee, Fedor Vitiugin, Mohamed Sherif, Carlos
    Castillo, and Axel-Cyrille Ngonga Ngomo. “Using Pre-Trained Language Models for
    Abstractive DBPEDIA Summarization: A Comparative Study.” In <i>SEMANTiCS</i>,
    2023.'
  ieee: 'H. M. A. Zahera, F. Vitiugin, M. Sherif, C. Castillo, and A.-C. Ngonga Ngomo,
    “Using Pre-trained Language Models for Abstractive DBPEDIA Summarization: A Comparative
    Study,” 2023.'
  mla: 'Zahera, Hamada Mohamed Abdelsamee, et al. “Using Pre-Trained Language Models
    for Abstractive DBPEDIA Summarization: A Comparative Study.” <i>SEMANTiCS</i>,
    2023.'
  short: 'H.M.A. Zahera, F. Vitiugin, M. Sherif, C. Castillo, A.-C. Ngonga Ngomo,
    in: SEMANTiCS, 2023.'
date_created: 2024-06-04T15:30:21Z
date_updated: 2024-06-04T15:31:02Z
department:
- _id: '574'
keyword:
- dice kiam ngonga porque sherif zahera
language:
- iso: eng
publication: SEMANTiCS
status: public
title: 'Using Pre-trained Language Models for Abstractive DBPEDIA Summarization: A
  Comparative Study'
type: conference
user_id: '67199'
year: '2023'
...
---
_id: '46518'
abstract:
- lang: eng
  text: "Purpose: This study addresses the limitations of current short abstracts
    of DBpedia entities, which often lack a comprehensive overview due to their creating
    method (i.e., selecting the first two-three sentences from the full DBpedia abstracts).\r\nMethodology:
    We leverage pre-trained language models to generate abstractive summaries of DBpedia
    abstracts in six languages (English, French, German, Italian, Spanish, and Dutch).
    We performed several experiments to assess the quality of generated summaries
    by language models. In particular, we evaluated the generated summaries using
    human judgments and automated metrics (Self-ROUGE and BERTScore). Additionally,
    we studied the correlation between human judgments and automated metrics in evaluating
    the generated summaries under different aspects: informativeness, coherence, conciseness,
    and fluency.\r\nFindings: Pre-trained language models generate summaries more
    concise and informative than existing short abstracts. Specifically, BART-based
    models effectively overcome the limitations of DBpedia short abstracts, especially
    for longer ones.\r\nMoreover, we show that BERTScore and ROUGE-1 are reliable
    metrics for assessing the informativeness and coherence of the generated summaries
    with respect to the full DBpedia abstracts. We also find a negative correlation
    between conciseness and human ratings. Furthermore, fluency evaluation remains
    challenging without human judgment.\r\nValue: This study has significant implications
    for various applications in machine learning and natural language processing that
    rely on DBpedia resources. By providing succinct and comprehensive summaries,
    our approach enhances the quality of DBpedia abstracts and contributes to the
    semantic web community"
author:
- first_name: Hamada Mohamed Abdelsamee
  full_name: Zahera, Hamada Mohamed Abdelsamee
  id: '72768'
  last_name: Zahera
  orcid: 0000-0003-0215-1278
- first_name: Fedor
  full_name: Vitiugin, Fedor
  last_name: Vitiugin
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Carlos
  full_name: Castillo, Carlos
  last_name: Castillo
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
citation:
  ama: 'Zahera HMA, Vitiugin F, Sherif M, Castillo C, Ngonga Ngomo A-C. Using Pre-trained
    Language Models for Abstractive DBpedia Summarization: A Comparative Study. In:
    <i>SEMANTiCS</i>. ; 2023.'
  apa: 'Zahera, H. M. A., Vitiugin, F., Sherif, M., Castillo, C., &#38; Ngonga Ngomo,
    A.-C. (2023). Using Pre-trained Language Models for Abstractive DBpedia Summarization:
    A Comparative Study. <i>SEMANTiCS</i>. SEMANTiCS 2023, Leipzig, Germany.'
  bibtex: '@inproceedings{Zahera_Vitiugin_Sherif_Castillo_Ngonga Ngomo_2023, title={Using
    Pre-trained Language Models for Abstractive DBpedia Summarization: A Comparative
    Study}, booktitle={SEMANTiCS}, author={Zahera, Hamada Mohamed Abdelsamee and Vitiugin,
    Fedor and Sherif, Mohamed and Castillo, Carlos and Ngonga Ngomo, Axel-Cyrille},
    year={2023} }'
  chicago: 'Zahera, Hamada Mohamed Abdelsamee, Fedor Vitiugin, Mohamed Sherif, Carlos
    Castillo, and Axel-Cyrille Ngonga Ngomo. “Using Pre-Trained Language Models for
    Abstractive DBpedia Summarization: A Comparative Study.” In <i>SEMANTiCS</i>,
    2023.'
  ieee: 'H. M. A. Zahera, F. Vitiugin, M. Sherif, C. Castillo, and A.-C. Ngonga Ngomo,
    “Using Pre-trained Language Models for Abstractive DBpedia Summarization: A Comparative
    Study,” presented at the SEMANTiCS 2023, Leipzig, Germany, 2023.'
  mla: 'Zahera, Hamada Mohamed Abdelsamee, et al. “Using Pre-Trained Language Models
    for Abstractive DBpedia Summarization: A Comparative Study.” <i>SEMANTiCS</i>,
    2023.'
  short: 'H.M.A. Zahera, F. Vitiugin, M. Sherif, C. Castillo, A.-C. Ngonga Ngomo,
    in: SEMANTiCS, 2023.'
conference:
  end_date: 2023-09-23
  location: Leipzig, Germany
  name: SEMANTiCS 2023
  start_date: 2023-09-20
date_created: 2023-08-16T08:58:12Z
date_updated: 2023-08-16T10:09:29Z
keyword:
- dice enexa kiam ngonga porque sherif zahera
language:
- iso: eng
publication: SEMANTiCS
status: public
title: 'Using Pre-trained Language Models for Abstractive DBpedia Summarization: A
  Comparative Study'
type: conference
user_id: '67234'
year: '2023'
...
---
_id: '33738'
author:
- first_name: Hamada Mohamed Abdelsamee
  full_name: Zahera, Hamada Mohamed Abdelsamee
  id: '72768'
  last_name: Zahera
  orcid: 0000-0003-0215-1278
- first_name: Stefan
  full_name: Heindorf, Stefan
  id: '11871'
  last_name: Heindorf
  orcid: 0000-0002-4525-6865
- first_name: Stefan
  full_name: Balke, Stefan
  last_name: Balke
- first_name: Jonas
  full_name: Haupt, Jonas
  last_name: Haupt
- first_name: Martin
  full_name: Voigt, Martin
  last_name: Voigt
- first_name: Carolin
  full_name: Walter, Carolin
  last_name: Walter
- first_name: Fabian
  full_name: Witter, Fabian
  last_name: Witter
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
citation:
  ama: 'Zahera HMA, Heindorf S, Balke S, et al. Tab2Onto: Unsupervised Semantification
    with Knowledge Graph Embeddings. In: <i>The Semantic Web: ESWC 2022 Satellite
    Events</i>. Springer International Publishing; 2022. doi:<a href="https://doi.org/10.1007/978-3-031-11609-4_9">10.1007/978-3-031-11609-4_9</a>'
  apa: 'Zahera, H. M. A., Heindorf, S., Balke, S., Haupt, J., Voigt, M., Walter, C.,
    Witter, F., &#38; Ngonga Ngomo, A.-C. (2022). Tab2Onto: Unsupervised Semantification
    with Knowledge Graph Embeddings. In <i>The Semantic Web: ESWC 2022 Satellite Events</i>.
    Springer International Publishing. <a href="https://doi.org/10.1007/978-3-031-11609-4_9">https://doi.org/10.1007/978-3-031-11609-4_9</a>'
  bibtex: '@inbook{Zahera_Heindorf_Balke_Haupt_Voigt_Walter_Witter_Ngonga Ngomo_2022,
    place={Cham}, title={Tab2Onto: Unsupervised Semantification with Knowledge Graph
    Embeddings}, DOI={<a href="https://doi.org/10.1007/978-3-031-11609-4_9">10.1007/978-3-031-11609-4_9</a>},
    booktitle={The Semantic Web: ESWC 2022 Satellite Events}, publisher={Springer
    International Publishing}, author={Zahera, Hamada Mohamed Abdelsamee and Heindorf,
    Stefan and Balke, Stefan and Haupt, Jonas and Voigt, Martin and Walter, Carolin
    and Witter, Fabian and Ngonga Ngomo, Axel-Cyrille}, year={2022} }'
  chicago: 'Zahera, Hamada Mohamed Abdelsamee, Stefan Heindorf, Stefan Balke, Jonas
    Haupt, Martin Voigt, Carolin Walter, Fabian Witter, and Axel-Cyrille Ngonga Ngomo.
    “Tab2Onto: Unsupervised Semantification with Knowledge Graph Embeddings.” In <i>The
    Semantic Web: ESWC 2022 Satellite Events</i>. Cham: Springer International Publishing,
    2022. <a href="https://doi.org/10.1007/978-3-031-11609-4_9">https://doi.org/10.1007/978-3-031-11609-4_9</a>.'
  ieee: 'H. M. A. Zahera <i>et al.</i>, “Tab2Onto: Unsupervised Semantification with
    Knowledge Graph Embeddings,” in <i>The Semantic Web: ESWC 2022 Satellite Events</i>,
    Cham: Springer International Publishing, 2022.'
  mla: 'Zahera, Hamada Mohamed Abdelsamee, et al. “Tab2Onto: Unsupervised Semantification
    with Knowledge Graph Embeddings.” <i>The Semantic Web: ESWC 2022 Satellite Events</i>,
    Springer International Publishing, 2022, doi:<a href="https://doi.org/10.1007/978-3-031-11609-4_9">10.1007/978-3-031-11609-4_9</a>.'
  short: 'H.M.A. Zahera, S. Heindorf, S. Balke, J. Haupt, M. Voigt, C. Walter, F.
    Witter, A.-C. Ngonga Ngomo, in: The Semantic Web: ESWC 2022 Satellite Events,
    Springer International Publishing, Cham, 2022.'
date_created: 2022-10-15T19:25:42Z
date_updated: 2023-06-23T09:20:20Z
department:
- _id: '574'
doi: 10.1007/978-3-031-11609-4_9
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://2022.eswc-conferences.org/wp-content/uploads/2022/05/pd_Zahera_et_al_paper_230.pdf
oa: '1'
place: Cham
publication: 'The Semantic Web: ESWC 2022 Satellite Events'
publication_identifier:
  isbn:
  - '9783031116087'
  - '9783031116094'
  issn:
  - 0302-9743
  - 1611-3349
publication_status: published
publisher: Springer International Publishing
status: public
title: 'Tab2Onto: Unsupervised Semantification with Knowledge Graph Embeddings'
type: book_chapter
user_id: '72768'
year: '2022'
...
---
_id: '46538'
author:
- first_name: Hamada Mohamed Abdelsamee
  full_name: Zahera, Hamada Mohamed Abdelsamee
  id: '72768'
  last_name: Zahera
  orcid: 0000-0003-0215-1278
- first_name: Daniel
  full_name: Vollmers, Daniel
  last_name: Vollmers
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
citation:
  ama: 'Zahera HMA, Vollmers D, Sherif M, Ngonga Ngomo A-C. MultPAX: Keyphrase Extraction
    using Language Models and Knowledge Graphs. In: <i>ISWC 2022</i>. Springer, Cham.
    doi:<a href="https://doi.org/10.1007/978-3-031-19433-7_18">10.1007/978-3-031-19433-7_18</a>'
  apa: 'Zahera, H. M. A., Vollmers, D., Sherif, M., &#38; Ngonga Ngomo, A.-C. (n.d.).
    MultPAX: Keyphrase Extraction using Language Models and Knowledge Graphs. <i>ISWC
    2022</i>. <a href="https://doi.org/10.1007/978-3-031-19433-7_18">https://doi.org/10.1007/978-3-031-19433-7_18</a>'
  bibtex: '@inproceedings{Zahera_Vollmers_Sherif_Ngonga Ngomo, title={MultPAX: Keyphrase
    Extraction using Language Models and Knowledge Graphs}, DOI={<a href="https://doi.org/10.1007/978-3-031-19433-7_18">10.1007/978-3-031-19433-7_18</a>},
    booktitle={ISWC 2022}, publisher={Springer, Cham}, author={Zahera, Hamada Mohamed
    Abdelsamee and Vollmers, Daniel and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}
    }'
  chicago: 'Zahera, Hamada Mohamed Abdelsamee, Daniel Vollmers, Mohamed Sherif, and
    Axel-Cyrille Ngonga Ngomo. “MultPAX: Keyphrase Extraction Using Language Models
    and Knowledge Graphs.” In <i>ISWC 2022</i>. Springer, Cham, n.d. <a href="https://doi.org/10.1007/978-3-031-19433-7_18">https://doi.org/10.1007/978-3-031-19433-7_18</a>.'
  ieee: 'H. M. A. Zahera, D. Vollmers, M. Sherif, and A.-C. Ngonga Ngomo, “MultPAX:
    Keyphrase Extraction using Language Models and Knowledge Graphs,” doi: <a href="https://doi.org/10.1007/978-3-031-19433-7_18">10.1007/978-3-031-19433-7_18</a>.'
  mla: 'Zahera, Hamada Mohamed Abdelsamee, et al. “MultPAX: Keyphrase Extraction Using
    Language Models and Knowledge Graphs.” <i>ISWC 2022</i>, Springer, Cham, doi:<a
    href="https://doi.org/10.1007/978-3-031-19433-7_18">10.1007/978-3-031-19433-7_18</a>.'
  short: 'H.M.A. Zahera, D. Vollmers, M. Sherif, A.-C. Ngonga Ngomo, in: ISWC 2022,
    Springer, Cham, n.d.'
date_created: 2023-08-16T10:16:05Z
date_updated: 2023-08-16T10:20:31Z
department:
- _id: '34'
doi: 10.1007/978-3-031-19433-7_18
keyword:
- colide dice eml4u ngonga raki sherif speaker vollmers zahera
language:
- iso: eng
publication: ISWC 2022
publication_identifier:
  eisbn:
  - 978-3-031-19433-7
  isbn:
  - 978-3-031-19432-0
publication_status: accepted
publisher: Springer, Cham
status: public
title: 'MultPAX: Keyphrase Extraction using Language Models and Knowledge Graphs'
type: conference
user_id: '67234'
year: '2022'
...
---
_id: '29291'
author:
- first_name: Hamada Mohamed Abdelsamee
  full_name: Zahera, Hamada Mohamed Abdelsamee
  id: '72768'
  last_name: Zahera
  orcid: 0000-0003-0215-1278
- first_name: Stefan
  full_name: Heindorf, Stefan
  id: '11871'
  last_name: Heindorf
  orcid: 0000-0002-4525-6865
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  last_name: Ngonga Ngomo
citation:
  ama: 'Zahera HMA, Heindorf S, Ngonga Ngomo A-C. ASSET: A Semi-supervised Approach
    for Entity Typing in Knowledge Graphs. In: <i>Proceedings of the 11th on Knowledge
    Capture Conference</i>. ACM; 2021. doi:<a href="https://doi.org/10.1145/3460210.3493563">10.1145/3460210.3493563</a>'
  apa: 'Zahera, H. M. A., Heindorf, S., &#38; Ngonga Ngomo, A.-C. (2021). ASSET: A
    Semi-supervised Approach for Entity Typing in Knowledge Graphs. <i>Proceedings
    of the 11th on Knowledge Capture Conference</i>. <a href="https://doi.org/10.1145/3460210.3493563">https://doi.org/10.1145/3460210.3493563</a>'
  bibtex: '@inproceedings{Zahera_Heindorf_Ngonga Ngomo_2021, title={ASSET: A Semi-supervised
    Approach for Entity Typing in Knowledge Graphs}, DOI={<a href="https://doi.org/10.1145/3460210.3493563">10.1145/3460210.3493563</a>},
    booktitle={Proceedings of the 11th on Knowledge Capture Conference}, publisher={ACM},
    author={Zahera, Hamada Mohamed Abdelsamee and Heindorf, Stefan and Ngonga Ngomo,
    Axel-Cyrille}, year={2021} }'
  chicago: 'Zahera, Hamada Mohamed Abdelsamee, Stefan Heindorf, and Axel-Cyrille Ngonga
    Ngomo. “ASSET: A Semi-Supervised Approach for Entity Typing in Knowledge Graphs.”
    In <i>Proceedings of the 11th on Knowledge Capture Conference</i>. ACM, 2021.
    <a href="https://doi.org/10.1145/3460210.3493563">https://doi.org/10.1145/3460210.3493563</a>.'
  ieee: 'H. M. A. Zahera, S. Heindorf, and A.-C. Ngonga Ngomo, “ASSET: A Semi-supervised
    Approach for Entity Typing in Knowledge Graphs,” 2021, doi: <a href="https://doi.org/10.1145/3460210.3493563">10.1145/3460210.3493563</a>.'
  mla: 'Zahera, Hamada Mohamed Abdelsamee, et al. “ASSET: A Semi-Supervised Approach
    for Entity Typing in Knowledge Graphs.” <i>Proceedings of the 11th on Knowledge
    Capture Conference</i>, ACM, 2021, doi:<a href="https://doi.org/10.1145/3460210.3493563">10.1145/3460210.3493563</a>.'
  short: 'H.M.A. Zahera, S. Heindorf, A.-C. Ngonga Ngomo, in: Proceedings of the 11th
    on Knowledge Capture Conference, ACM, 2021.'
date_created: 2022-01-12T10:27:02Z
date_updated: 2024-05-30T18:02:43Z
ddc:
- '000'
department:
- _id: '574'
doi: 10.1145/3460210.3493563
file:
- access_level: closed
  content_type: application/pdf
  creator: heindorf
  date_created: 2024-05-30T18:02:28Z
  date_updated: 2024-05-30T18:02:28Z
  file_id: '54523'
  file_name: ASSET_public.pdf
  file_size: 537882
  relation: main_file
  success: 1
file_date_updated: 2024-05-30T18:02:28Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://papers.dice-research.org/2021/KCAP2021_ASSET/public.pdf
oa: '1'
publication: Proceedings of the 11th on Knowledge Capture Conference
publication_status: published
publisher: ACM
status: public
title: 'ASSET: A Semi-supervised Approach for Entity Typing in Knowledge Graphs'
type: conference
user_id: '11871'
year: '2021'
...
---
_id: '29043'
abstract:
- lang: eng
  text: 'Social media plays a significant role in disaster management by providing
    valuable data about affected people, donations and help requests. Recent studies
    highlight the need to filter information on social media into fine-grained content
    labels. However, identifying useful information from massive amounts of social
    media posts during a crisis is a challenging task. In this paper, we propose I-AID,
    a multimodel approach to automatically categorize tweets into multi-label information
    types and filter critical information from the enormous volume of social media
    data. I-AID incorporates three main components: i) a BERT- based encoder to capture
    the semantics of a tweet and represent as a low-dimensional vector, ii) a graph
    attention network (GAT) to apprehend correlations between tweets’ words/entities
    and the corresponding information types, and iii) a Relation Network as a learnable
    distance metric to compute the similarity between tweets and their corresponding
    information types in a supervised way. We conducted several experiments on two
    real publicly-available datasets. Our results indicate that I-AID outperforms
    state-of- the-art approaches in terms of weighted average F1 score by +6% and
    +4% on the TREC-IS dataset and COVID-19 Tweets, respectively.'
author:
- first_name: Hamada Mohamed Abdelsamee
  full_name: Zahera, Hamada Mohamed Abdelsamee
  id: '72768'
  last_name: Zahera
  orcid: 0000-0003-0215-1278
- first_name: Rricha
  full_name: Jalota, Rricha
  id: '69526'
  last_name: Jalota
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
citation:
  ama: 'Zahera HMA, Jalota R, Sherif M, Ngonga Ngomo A-C. I-AID: Identifying Actionable
    Information from Disaster-related Tweets. In: <i>IEEE Open Access</i>. ; 2021.'
  apa: 'Zahera, H. M. A., Jalota, R., Sherif, M., &#38; Ngonga Ngomo, A.-C. (2021).
    I-AID: Identifying Actionable Information from Disaster-related Tweets. <i>IEEE
    Open Access</i>.'
  bibtex: '@inproceedings{Zahera_Jalota_Sherif_Ngonga Ngomo_2021, title={I-AID: Identifying
    Actionable Information from Disaster-related Tweets}, booktitle={IEEE Open Access},
    author={Zahera, Hamada Mohamed Abdelsamee and Jalota, Rricha and Sherif, Mohamed
    and Ngonga Ngomo, Axel-Cyrille}, year={2021} }'
  chicago: 'Zahera, Hamada Mohamed Abdelsamee, Rricha Jalota, Mohamed Sherif, and
    Axel-Cyrille Ngonga Ngomo. “I-AID: Identifying Actionable Information from Disaster-Related
    Tweets.” In <i>IEEE Open Access</i>, 2021.'
  ieee: 'H. M. A. Zahera, R. Jalota, M. Sherif, and A.-C. Ngonga Ngomo, “I-AID: Identifying
    Actionable Information from Disaster-related Tweets,” 2021.'
  mla: 'Zahera, Hamada Mohamed Abdelsamee, et al. “I-AID: Identifying Actionable Information
    from Disaster-Related Tweets.” <i>IEEE Open Access</i>, 2021.'
  short: 'H.M.A. Zahera, R. Jalota, M. Sherif, A.-C. Ngonga Ngomo, in: IEEE Open Access,
    2021.'
date_created: 2021-12-17T10:06:30Z
date_updated: 2023-08-16T09:35:42Z
keyword:
- sys:relevantFor:infai sys:relevantFor:DAIKIRI ngonga zahera sherif daikiriproject
  dice simba
language:
- iso: eng
publication: IEEE Open Access
status: public
title: 'I-AID: Identifying Actionable Information from Disaster-related Tweets'
type: conference
user_id: '67234'
year: '2021'
...
---
_id: '29044'
author:
- first_name: Jaydeep
  full_name: Chakraborty, Jaydeep
  last_name: Chakraborty
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Hamada Mohamed Abdelsamee
  full_name: Zahera, Hamada Mohamed Abdelsamee
  id: '72768'
  last_name: Zahera
  orcid: 0000-0003-0215-1278
- first_name: Srividya
  full_name: Bansal, Srividya
  last_name: Bansal
citation:
  ama: 'Chakraborty J, Sherif M, Zahera HMA, Bansal S. OntoConnect: Domain-Agnostic
    Ontology Alignment using Graph Embedding with Negative Sampling. In: <i>Proceedings
    of the IEEE International Conference on Machine Learning and Applications</i>.
    ; 2021.'
  apa: 'Chakraborty, J., Sherif, M., Zahera, H. M. A., &#38; Bansal, S. (2021). OntoConnect:
    Domain-Agnostic Ontology Alignment using Graph Embedding with Negative Sampling.
    <i>Proceedings of the IEEE International Conference on Machine Learning and Applications</i>.'
  bibtex: '@inproceedings{Chakraborty_Sherif_Zahera_Bansal_2021, title={OntoConnect:
    Domain-Agnostic Ontology Alignment using Graph Embedding with Negative Sampling},
    booktitle={Proceedings of the IEEE International Conference on Machine Learning
    and Applications}, author={Chakraborty, Jaydeep and Sherif, Mohamed and Zahera,
    Hamada Mohamed Abdelsamee and Bansal, Srividya}, year={2021} }'
  chicago: 'Chakraborty, Jaydeep, Mohamed Sherif, Hamada Mohamed Abdelsamee Zahera,
    and Srividya Bansal. “OntoConnect: Domain-Agnostic Ontology Alignment Using Graph
    Embedding with Negative Sampling.” In <i>Proceedings of the IEEE International
    Conference on Machine Learning and Applications</i>, 2021.'
  ieee: 'J. Chakraborty, M. Sherif, H. M. A. Zahera, and S. Bansal, “OntoConnect:
    Domain-Agnostic Ontology Alignment using Graph Embedding with Negative Sampling,”
    2021.'
  mla: 'Chakraborty, Jaydeep, et al. “OntoConnect: Domain-Agnostic Ontology Alignment
    Using Graph Embedding with Negative Sampling.” <i>Proceedings of the IEEE International
    Conference on Machine Learning and Applications</i>, 2021.'
  short: 'J. Chakraborty, M. Sherif, H.M.A. Zahera, S. Bansal, in: Proceedings of
    the IEEE International Conference on Machine Learning and Applications, 2021.'
date_created: 2021-12-17T10:06:45Z
date_updated: 2023-08-16T10:25:55Z
keyword:
- dice sherif hamada
language:
- iso: eng
publication: Proceedings of the IEEE International Conference on Machine Learning
  and Applications
status: public
title: 'OntoConnect: Domain-Agnostic Ontology Alignment using Graph Embedding with
  Negative Sampling'
type: conference
user_id: '67234'
year: '2021'
...
---
_id: '29040'
author:
- first_name: Hamada Mohamed Abdelsamee
  full_name: Zahera, Hamada Mohamed Abdelsamee
  id: '72768'
  last_name: Zahera
  orcid: 0000-0003-0215-1278
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
citation:
  ama: 'Zahera HMA, Sherif M. ProBERT: Product Data Classification with Fine-tuning
    BERT Model. In: <i>Proceedings of Mining the Web of HTML-Embedded Product Data
    Workshop (MWPD2020)</i>. ; 2020.'
  apa: 'Zahera, H. M. A., &#38; Sherif, M. (2020). ProBERT: Product Data Classification
    with Fine-tuning BERT Model. <i>Proceedings of Mining the Web of HTML-Embedded
    Product Data Workshop (MWPD2020)</i>.'
  bibtex: '@inproceedings{Zahera_Sherif_2020, title={ProBERT: Product Data Classification
    with Fine-tuning BERT Model}, booktitle={Proceedings of Mining the Web of HTML-embedded
    Product Data Workshop (MWPD2020)}, author={Zahera, Hamada Mohamed Abdelsamee and
    Sherif, Mohamed}, year={2020} }'
  chicago: 'Zahera, Hamada Mohamed Abdelsamee, and Mohamed Sherif. “ProBERT: Product
    Data Classification with Fine-Tuning BERT Model.” In <i>Proceedings of Mining
    the Web of HTML-Embedded Product Data Workshop (MWPD2020)</i>, 2020.'
  ieee: 'H. M. A. Zahera and M. Sherif, “ProBERT: Product Data Classification with
    Fine-tuning BERT Model,” 2020.'
  mla: 'Zahera, Hamada Mohamed Abdelsamee, and Mohamed Sherif. “ProBERT: Product Data
    Classification with Fine-Tuning BERT Model.” <i>Proceedings of Mining the Web
    of HTML-Embedded Product Data Workshop (MWPD2020)</i>, 2020.'
  short: 'H.M.A. Zahera, M. Sherif, in: Proceedings of Mining the Web of HTML-Embedded
    Product Data Workshop (MWPD2020), 2020.'
date_created: 2021-12-17T10:05:42Z
date_updated: 2023-08-16T10:06:10Z
keyword:
- 2020 dice zahera sherif knowgraphs sys:relevantFor:limboproject limboproject sys:relevantFor:infai
  sys:relevantFor:bis limes limbo opal
language:
- iso: eng
publication: Proceedings of Mining the Web of HTML-embedded Product Data Workshop
  (MWPD2020)
status: public
title: 'ProBERT: Product Data Classification with Fine-tuning BERT Model'
type: conference
user_id: '67234'
year: '2020'
...
---
_id: '29037'
abstract:
- lang: eng
  text: Existing technologies employ different machine learning approaches to predict
    disasters from historical environmental data. However, for short-term disasters
    (e.g., earthquakes), historical data alone has a limited prediction capability.
    In this work, we consider social media as a supplementary source of knowledge
    in addition to historical environmental data. Further, we build a joint model
    that learns from disaster-related tweets and environmental data to improve prediction.
    We propose the combination of semantically-enriched word embedding to represent
    entities in tweets with their semantics representations computed with the traditional
    word2vec. Our experiments show that our proposed approach outperforms the accuracy
    of state-of-the-art models in disaster prediction.
author:
- first_name: Hamada Mohamed Abdelsamee
  full_name: Zahera, Hamada Mohamed Abdelsamee
  id: '72768'
  last_name: Zahera
  orcid: 0000-0003-0215-1278
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Axel-Cyrille
  full_name: Ngonga Ngomo, Axel-Cyrille
  id: '65716'
  last_name: Ngonga Ngomo
citation:
  ama: 'Zahera HMA, Sherif M, Ngonga Ngomo A-C. Jointly Learning from Social Media
    and Environmental Data for Typhoon Intensity Prediction. In: <i>K-CAP 2019: Knowledge
    Capture Conference</i>. ; 2019:4.'
  apa: 'Zahera, H. M. A., Sherif, M., &#38; Ngonga Ngomo, A.-C. (2019). Jointly Learning
    from Social Media and Environmental Data for Typhoon Intensity Prediction. <i>K-CAP
    2019: Knowledge Capture Conference</i>, 4.'
  bibtex: '@inproceedings{Zahera_Sherif_Ngonga Ngomo_2019, title={Jointly Learning
    from Social Media and Environmental Data for Typhoon Intensity Prediction}, booktitle={K-CAP
    2019: Knowledge Capture Conference}, author={Zahera, Hamada Mohamed Abdelsamee
    and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2019}, pages={4} }'
  chicago: 'Zahera, Hamada Mohamed Abdelsamee, Mohamed Sherif, and Axel-Cyrille Ngonga
    Ngomo. “Jointly Learning from Social Media and Environmental Data for Typhoon
    Intensity Prediction.” In <i>K-CAP 2019: Knowledge Capture Conference</i>, 4,
    2019.'
  ieee: 'H. M. A. Zahera, M. Sherif, and A.-C. Ngonga Ngomo, “Jointly Learning from
    Social Media and Environmental Data for Typhoon Intensity Prediction,” in <i>K-CAP
    2019: Knowledge Capture Conference</i>, 2019, p. 4.'
  mla: 'Zahera, Hamada Mohamed Abdelsamee, et al. “Jointly Learning from Social Media
    and Environmental Data for Typhoon Intensity Prediction.” <i>K-CAP 2019: Knowledge
    Capture Conference</i>, 2019, p. 4.'
  short: 'H.M.A. Zahera, M. Sherif, A.-C. Ngonga Ngomo, in: K-CAP 2019: Knowledge
    Capture Conference, 2019, p. 4.'
date_created: 2021-12-17T10:05:07Z
date_updated: 2023-08-16T09:24:21Z
keyword:
- sys:relevantFor:infai sys:relevantFor:bis sys:relevantFor:simba ngonga simba zahera
  sherif solide limboproject opal group\_aksw dice
language:
- iso: eng
page: '4'
publication: 'K-CAP 2019: Knowledge Capture Conference'
status: public
title: Jointly Learning from Social Media and Environmental Data for Typhoon Intensity
  Prediction
type: conference
user_id: '67234'
year: '2019'
...
---
_id: '29003'
abstract:
- lang: eng
  text: In this paper, we describe our approach to classify disaster-related tweets
    into multilabel information types (ie, labels). We aim to filter first relevant
    tweets during disasters. Then, we assign tweets relevant information types. Information
    types can be SearchAndRescue, MovePeople and Volunteer. We employ a fine-tuned
    BERT model with 10 BERT layers. Further, we submitted our approach to the TREC-IS
    2019 challenge, the evaluation results showed that our approach outperforms the
    F1-score of median score in identifying actionable information.
author:
- first_name: Hamada Mohamed Abdelsamee
  full_name: Zahera, Hamada Mohamed Abdelsamee
  id: '72768'
  last_name: Zahera
  orcid: 0000-0003-0215-1278
- first_name: Ibrahim
  full_name: A. Elgendy, Ibrahim
  last_name: A. Elgendy
- first_name: Rricha
  full_name: Jalota, Rricha
  id: '69526'
  last_name: Jalota
- first_name: Mohamed
  full_name: Sherif, Mohamed
  id: '67234'
  last_name: Sherif
  orcid: https://orcid.org/0000-0002-9927-2203
citation:
  ama: 'Zahera HMA, A. Elgendy I, Jalota R, Sherif M. Fine-tuned BERT Model for Multi-Label
    Tweets Classification. In: <i>Proceedings of the Twenty-Eighth Text REtrieval
    Conference, {TREC} 2019, Gaithersburg, Maryland, USA, November 13-15, 2019</i>.
    ; 2019.'
  apa: Zahera, H. M. A., A. Elgendy, I., Jalota, R., &#38; Sherif, M. (2019). Fine-tuned
    BERT Model for Multi-Label Tweets Classification. <i>Proceedings of the Twenty-Eighth
    Text REtrieval Conference, {TREC} 2019, Gaithersburg, Maryland, USA, November
    13-15, 2019</i>.
  bibtex: '@inproceedings{Zahera_A. Elgendy_Jalota_Sherif_2019, title={Fine-tuned
    BERT Model for Multi-Label Tweets Classification}, booktitle={Proceedings of the
    Twenty-Eighth Text REtrieval Conference, {TREC} 2019, Gaithersburg, Maryland,
    USA, November 13-15, 2019}, author={Zahera, Hamada Mohamed Abdelsamee and A. Elgendy,
    Ibrahim and Jalota, Rricha and Sherif, Mohamed}, year={2019} }'
  chicago: Zahera, Hamada Mohamed Abdelsamee, Ibrahim A. Elgendy, Rricha Jalota, and
    Mohamed Sherif. “Fine-Tuned BERT Model for Multi-Label Tweets Classification.”
    In <i>Proceedings of the Twenty-Eighth Text REtrieval Conference, {TREC} 2019,
    Gaithersburg, Maryland, USA, November 13-15, 2019</i>, 2019.
  ieee: H. M. A. Zahera, I. A. Elgendy, R. Jalota, and M. Sherif, “Fine-tuned BERT
    Model for Multi-Label Tweets Classification,” 2019.
  mla: Zahera, Hamada Mohamed Abdelsamee, et al. “Fine-Tuned BERT Model for Multi-Label
    Tweets Classification.” <i>Proceedings of the Twenty-Eighth Text REtrieval Conference,
    {TREC} 2019, Gaithersburg, Maryland, USA, November 13-15, 2019</i>, 2019.
  short: 'H.M.A. Zahera, I. A. Elgendy, R. Jalota, M. Sherif, in: Proceedings of the
    Twenty-Eighth Text REtrieval Conference, {TREC} 2019, Gaithersburg, Maryland,
    USA, November 13-15, 2019, 2019.'
date_created: 2021-12-17T09:48:17Z
date_updated: 2023-08-16T09:25:34Z
keyword:
- zahera elgendy jalota sherif dice
language:
- iso: eng
publication: Proceedings of the Twenty-Eighth Text REtrieval Conference, {TREC} 2019,
  Gaithersburg, Maryland, USA, November 13-15, 2019
status: public
title: Fine-tuned BERT Model for Multi-Label Tweets Classification
type: conference
user_id: '67234'
year: '2019'
...