---
_id: '46516'
abstract:
- lang: eng
  text: Linked knowledge graphs build the backbone of many data-driven applications
    such as search engines, conversational agents and e-commerce solutions. Declarative
    link discovery frameworks use complex link specifications to express the conditions
    under which a link between two resources can be deemed to exist. However, understanding
    such complex link specifications is a challenging task for non-expert users of
    link discovery frameworks. In this paper, we address this drawback by devising
    NMV-LS, a language model-based verbalization approach for translating complex
    link specifications into natural language. NMV-LS relies on the results of rule-based
    link specification verbalization to apply continuous training on T5, a large language
    model based on the Transformerarchitecture. We evaluated NMV-LS on English and
    German datasets using well-known machine translation metrics such as BLUE, METEOR,
    ChrF++ and TER. Our results suggest that our approach achieves a verbalization
    performance close to that of humans and outperforms state of the art approaches.
    Our source code and datasets are publicly available at https://github.com/dice-group/NMV-LS.
author:
- first_name: Abdullah Fathi Ahmed
  full_name: Ahmed, Abdullah Fathi Ahmed
  id: '29670'
  last_name: Ahmed
- first_name: Asep Fajar
  full_name: Firmansyah, Asep Fajar
  id: '76787'
  last_name: Firmansyah
- 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: 'Ahmed AFA, Firmansyah AF, Sherif M, Moussallem D, Ngonga Ngomo A-C. Explainable
    Integration of Knowledge Graphs Using Large Language Models. In: <i>Natural Language
    Processing and Information Systems</i>. Springer Nature Switzerland; 2023. doi:<a
    href="https://doi.org/10.1007/978-3-031-35320-8_9">10.1007/978-3-031-35320-8_9</a>'
  apa: Ahmed, A. F. A., Firmansyah, A. F., Sherif, M., Moussallem, D., &#38; Ngonga
    Ngomo, A.-C. (2023). Explainable Integration of Knowledge Graphs Using Large Language
    Models. In <i>Natural Language Processing and Information Systems</i>. Springer
    Nature Switzerland. <a href="https://doi.org/10.1007/978-3-031-35320-8_9">https://doi.org/10.1007/978-3-031-35320-8_9</a>
  bibtex: '@inbook{Ahmed_Firmansyah_Sherif_Moussallem_Ngonga Ngomo_2023, place={Cham},
    title={Explainable Integration of Knowledge Graphs Using Large Language Models},
    DOI={<a href="https://doi.org/10.1007/978-3-031-35320-8_9">10.1007/978-3-031-35320-8_9</a>},
    booktitle={Natural Language Processing and Information Systems}, publisher={Springer
    Nature Switzerland}, author={Ahmed, Abdullah Fathi Ahmed and Firmansyah, Asep
    Fajar and Sherif, Mohamed and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille},
    year={2023} }'
  chicago: 'Ahmed, Abdullah Fathi Ahmed, Asep Fajar Firmansyah, Mohamed Sherif, Diego
    Moussallem, and Axel-Cyrille Ngonga Ngomo. “Explainable Integration of Knowledge
    Graphs Using Large Language Models.” In <i>Natural Language Processing and Information
    Systems</i>. Cham: Springer Nature Switzerland, 2023. <a href="https://doi.org/10.1007/978-3-031-35320-8_9">https://doi.org/10.1007/978-3-031-35320-8_9</a>.'
  ieee: 'A. F. A. Ahmed, A. F. Firmansyah, M. Sherif, D. Moussallem, and A.-C. Ngonga
    Ngomo, “Explainable Integration of Knowledge Graphs Using Large Language Models,”
    in <i>Natural Language Processing and Information Systems</i>, Cham: Springer
    Nature Switzerland, 2023.'
  mla: Ahmed, Abdullah Fathi Ahmed, et al. “Explainable Integration of Knowledge Graphs
    Using Large Language Models.” <i>Natural Language Processing and Information Systems</i>,
    Springer Nature Switzerland, 2023, doi:<a href="https://doi.org/10.1007/978-3-031-35320-8_9">10.1007/978-3-031-35320-8_9</a>.
  short: 'A.F.A. Ahmed, A.F. Firmansyah, M. Sherif, D. Moussallem, A.-C. Ngonga Ngomo,
    in: Natural Language Processing and Information Systems, Springer Nature Switzerland,
    Cham, 2023.'
date_created: 2023-08-16T08:57:11Z
date_updated: 2024-06-04T12:23:45Z
department:
- _id: '34'
- _id: '574'
doi: 10.1007/978-3-031-35320-8_9
language:
- iso: eng
place: Cham
publication: Natural Language Processing and Information Systems
publication_identifier:
  isbn:
  - '9783031353192'
  - '9783031353208'
  issn:
  - 0302-9743
  - 1611-3349
publication_status: published
publisher: Springer Nature Switzerland
status: public
title: Explainable Integration of Knowledge Graphs Using Large Language Models
type: book_chapter
user_id: '76787'
year: '2023'
...
---
_id: '46572'
abstract:
- lang: eng
  text: Indonesian is classified as underrepresented in the Natural Language Processing
    (NLP) field, despite being the tenth most spoken language in the world with 198
    million speakers. The paucity of datasets is recognized as the main reason for
    the slow advancements in NLP research for underrepresented languages. Significant
    attempts were made in 2020 to address this drawback for Indonesian. The Indonesian
    Natural Language Understanding (IndoNLU) benchmark was introduced alongside IndoBERT
    pre-trained language model. The second benchmark, Indonesian Language Evaluation
    Montage (IndoLEM), was presented in the same year. These benchmarks support several
    tasks, including Named Entity Recognition (NER). However, all NER datasets are
    in the public domain and do not contain domain-specific datasets. To alleviate
    this drawback, we introduce IndQNER, a manually annotated NER benchmark dataset
    in the religious domain that adheres to a meticulously designed annotation guideline.
    Since Indonesia has the world’s largest Muslim population, we build the dataset
    from the Indonesian translation of the Quran. The dataset includes 2475 named
    entities representing 18 different classes. To assess the annotation quality of
    IndQNER, we perform experiments with BiLSTM and CRF-based NER, as well as IndoBERT
    fine-tuning. The results reveal that the first model outperforms the second model
    achieving 0.98 F1 points. This outcome indicates that IndQNER may be an acceptable
    evaluation metric for Indonesian NER tasks in the aforementioned domain, widening
    the research’s domain range.
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: 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: 'Gusmita RH, Firmansyah AF, Moussallem D, Ngonga Ngomo A-C. IndQNER: Named
    Entity Recognition Benchmark Dataset from the Indonesian Translation of the Quran.
    In: <i>Natural Language Processing and Information Systems</i>. Springer Nature
    Switzerland; 2023. doi:<a href="https://doi.org/10.1007/978-3-031-35320-8_12">10.1007/978-3-031-35320-8_12</a>'
  apa: 'Gusmita, R. H., Firmansyah, A. F., Moussallem, D., &#38; Ngonga Ngomo, A.-C.
    (2023). IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian
    Translation of the Quran. In <i>Natural Language Processing and Information Systems</i>.
    International Conference on Applications of Natural Language to Information Systems
    (NLDB) 2023, Derby, UK. Springer Nature Switzerland. <a href="https://doi.org/10.1007/978-3-031-35320-8_12">https://doi.org/10.1007/978-3-031-35320-8_12</a>'
  bibtex: '@inbook{Gusmita_Firmansyah_Moussallem_Ngonga Ngomo_2023, place={Cham},
    title={IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian
    Translation of the Quran}, DOI={<a href="https://doi.org/10.1007/978-3-031-35320-8_12">10.1007/978-3-031-35320-8_12</a>},
    booktitle={Natural Language Processing and Information Systems}, publisher={Springer
    Nature Switzerland}, author={Gusmita, Ria Hari and Firmansyah, Asep Fajar and
    Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}, year={2023} }'
  chicago: 'Gusmita, Ria Hari, Asep Fajar Firmansyah, Diego Moussallem, and Axel-Cyrille
    Ngonga Ngomo. “IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian
    Translation of the Quran.” In <i>Natural Language Processing and Information Systems</i>.
    Cham: Springer Nature Switzerland, 2023. <a href="https://doi.org/10.1007/978-3-031-35320-8_12">https://doi.org/10.1007/978-3-031-35320-8_12</a>.'
  ieee: 'R. H. Gusmita, A. F. Firmansyah, D. Moussallem, and A.-C. Ngonga Ngomo, “IndQNER:
    Named Entity Recognition Benchmark Dataset from the Indonesian Translation of the Quran,”
    in <i>Natural Language Processing and Information Systems</i>, Cham: Springer
    Nature Switzerland, 2023.'
  mla: 'Gusmita, Ria Hari, et al. “IndQNER: Named Entity Recognition Benchmark Dataset
    from the Indonesian Translation of the Quran.” <i>Natural Language Processing
    and Information Systems</i>, Springer Nature Switzerland, 2023, doi:<a href="https://doi.org/10.1007/978-3-031-35320-8_12">10.1007/978-3-031-35320-8_12</a>.'
  short: 'R.H. Gusmita, A.F. Firmansyah, D. Moussallem, A.-C. Ngonga Ngomo, in: Natural
    Language Processing and Information Systems, Springer Nature Switzerland, Cham,
    2023.'
conference:
  end_date: 2023-06-23
  location: Derby, UK
  name: International Conference on Applications of Natural Language to Information
    Systems (NLDB) 2023
  start_date: 2023-06-21
date_created: 2023-08-17T12:41:45Z
date_updated: 2024-11-19T15:41:34Z
department:
- _id: '34'
- _id: '574'
doi: 10.1007/978-3-031-35320-8_12
keyword:
- NER benchmark dataset
- Indonesian
- specific domain
language:
- iso: eng
place: Cham
publication: Natural Language Processing and Information Systems
publication_identifier:
  isbn:
  - '9783031353192'
  - '9783031353208'
  issn:
  - 0302-9743
  - 1611-3349
publication_status: published
publisher: Springer Nature Switzerland
related_material:
  link:
  - relation: confirmation
    url: https://link.springer.com/chapter/10.1007/978-3-031-35320-8_12
status: public
title: 'IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian Translation
  of the Quran'
type: book_chapter
user_id: '71039'
year: '2023'
...