---
_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.
Data & Knowledge Engineering. 2026;161:102504. doi:https://doi.org/10.1016/j.datak.2025.102504'
apa: 'Gusmita, R. H., Firmansyah, A. F., Zahera, H. M. A., & Ngonga Ngomo, A.-C.
(2026). ELEVATE-ID: Extending Large Language Models for End-to-End Entity Linking
Evaluation in Indonesian. Data & Knowledge Engineering, 161,
102504. https://doi.org/10.1016/j.datak.2025.102504'
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={https://doi.org/10.1016/j.datak.2025.102504},
journal={Data & 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.” Data & Knowledge Engineering
161 (2026): 102504. https://doi.org/10.1016/j.datak.2025.102504.'
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,” Data & Knowledge Engineering, vol. 161, p. 102504,
2026, doi: https://doi.org/10.1016/j.datak.2025.102504.'
mla: 'Gusmita, Ria Hari, et al. “ELEVATE-ID: Extending Large Language Models for
End-to-End Entity Linking Evaluation in Indonesian.” Data & Knowledge Engineering,
vol. 161, 2026, p. 102504, doi:https://doi.org/10.1016/j.datak.2025.102504.'
short: R.H. Gusmita, A.F. Firmansyah, H.M.A. Zahera, A.-C. Ngonga Ngomo, Data &
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: '56581'
abstract:
- lang: eng
text: 'In recent years, there has been a surge in natural language processing research
focused on low-resource languages (LrLs), underscoring the growing recognition
that LrLs deserve the same attention as high-resource languages (HrLs). This shift
is crucial for ensuring linguistic diversity and inclusivity in the digital age.
Despite Indonesian ranking as the 11th most spoken language globally, it remains
under-resourced in terms of computational tools and datasets. Within the semantic
web domain, Entity Linking (EL) is pivotal, linking textual entity mentions to
their corresponding entries in knowledge bases. This process is foundational for
advanced information extraction tasks, including relation extraction and event
detection. To bolster EL research in Indonesian, we introduce IndEL, the first
benchmark dataset tailored for both general and specific domains. IndEL was manually
curated using Wikidata, adhering to a rigorous set of annotation guidelines. We
used two Named Entity Recognition (NER) benchmark datasets for entity extraction:
NER UI for the general domain and IndQNER for the specific domain. IndQNER focused
on entities from the Indonesian translation of the Quran. IndEL comprises 4765
entities in the general domain and 2453 in the specific domain. Using the GERBIL
framework, we use IndEL to evaluate the performance of various EL systems, such
as Babelfy, DBpedia Spotlight, MAG, OpenTapioca, and WAT. Our further investigation
reveals that within Wikidata, a significant number of NIL entities remain unlinked
due to the limited number of Indonesian labels and the use of acronyms. Especially
in the specific domain, transliteration and translation processes performed to
create the Indonesian translation of the Quran contribute to the presence of entities
in a descriptive form and as synonyms.'
author:
- first_name: Ria Hari
full_name: Gusmita, Ria Hari
id: '71039'
last_name: Gusmita
- first_name: Muhammad Faruq Amiral
full_name: Abshar, Muhammad Faruq Amiral
last_name: Abshar
- 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, Abshar MFA, Moussallem D, Ngonga Ngomo A-C. IndEL: Indonesian
Entity Linking Benchmark Dataset for General and Specific Domains. In: Lecture
Notes in Computer Science. Springer Nature Switzerland; 2024. doi:10.1007/978-3-031-70239-6_34'
apa: 'Gusmita, R. H., Abshar, M. F. A., Moussallem, D., & Ngonga Ngomo, A.-C.
(2024). IndEL: Indonesian Entity Linking Benchmark Dataset for General and Specific
Domains. In Lecture Notes in Computer Science. The 29th Annual International
Conference on Natural Language & Information Systems (NLDB 2024), Turin, Italy.
Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-70239-6_34'
bibtex: '@inbook{Gusmita_Abshar_Moussallem_Ngonga Ngomo_2024, place={Cham}, title={IndEL:
Indonesian Entity Linking Benchmark Dataset for General and Specific Domains},
DOI={10.1007/978-3-031-70239-6_34},
booktitle={Lecture Notes in Computer Science}, publisher={Springer Nature Switzerland},
author={Gusmita, Ria Hari and Abshar, Muhammad Faruq Amiral and Moussallem, Diego
and Ngonga Ngomo, Axel-Cyrille}, year={2024} }'
chicago: 'Gusmita, Ria Hari, Muhammad Faruq Amiral Abshar, Diego Moussallem, and
Axel-Cyrille Ngonga Ngomo. “IndEL: Indonesian Entity Linking Benchmark Dataset
for General and Specific Domains.” In Lecture Notes in Computer Science.
Cham: Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-70239-6_34.'
ieee: 'R. H. Gusmita, M. F. A. Abshar, D. Moussallem, and A.-C. Ngonga Ngomo, “IndEL:
Indonesian Entity Linking Benchmark Dataset for General and Specific Domains,”
in Lecture Notes in Computer Science, Cham: Springer Nature Switzerland,
2024.'
mla: 'Gusmita, Ria Hari, et al. “IndEL: Indonesian Entity Linking Benchmark Dataset
for General and Specific Domains.” Lecture Notes in Computer Science, Springer
Nature Switzerland, 2024, doi:10.1007/978-3-031-70239-6_34.'
short: 'R.H. Gusmita, M.F.A. Abshar, D. Moussallem, A.-C. Ngonga Ngomo, in: Lecture
Notes in Computer Science, Springer Nature Switzerland, Cham, 2024.'
conference:
end_date: 2024-06-27
location: Turin, Italy
name: The 29th Annual International Conference on Natural Language & Information
Systems (NLDB 2024)
start_date: 2024-06-25
date_created: 2024-10-10T14:29:08Z
date_updated: 2024-10-14T19:22:16Z
doi: 10.1007/978-3-031-70239-6_34
keyword:
- entity linking benchmark dataset
- Indonesian
- general and specific domains
language:
- iso: eng
place: Cham
publication: Lecture Notes in Computer Science
publication_identifier:
isbn:
- '9783031702389'
- '9783031702396'
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer Nature Switzerland
related_material:
link:
- relation: confirmation
url: https://link.springer.com/chapter/10.1007/978-3-031-70239-6_34
status: public
title: 'IndEL: Indonesian Entity Linking Benchmark Dataset for General and Specific
Domains'
type: book_chapter
user_id: '71039'
year: '2024'
...
---
_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: Natural Language Processing and Information Systems. Springer Nature
Switzerland; 2023. doi:10.1007/978-3-031-35320-8_12'
apa: 'Gusmita, R. H., Firmansyah, A. F., Moussallem, D., & Ngonga Ngomo, A.-C.
(2023). IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian
Translation of the Quran. In Natural Language Processing and Information Systems.
International Conference on Applications of Natural Language to Information Systems
(NLDB) 2023, Derby, UK. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-35320-8_12'
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={10.1007/978-3-031-35320-8_12},
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 Natural Language Processing and Information Systems.
Cham: Springer Nature Switzerland, 2023. https://doi.org/10.1007/978-3-031-35320-8_12.'
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 Natural Language Processing and Information Systems, Cham: Springer
Nature Switzerland, 2023.'
mla: 'Gusmita, Ria Hari, et al. “IndQNER: Named Entity Recognition Benchmark Dataset
from the Indonesian Translation of the Quran.” Natural Language Processing
and Information Systems, Springer Nature Switzerland, 2023, doi:10.1007/978-3-031-35320-8_12.'
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'
...
---
_id: '56579'
abstract:
- lang: eng
text: Question answering engines have become one of the most popular type of applications
driven by Semantic Web technologies. Consequently, the provision of means to quantify
the performance of current question answering approaches on current datasets has
become ever more important. However, a large percentage of the queries found in
popular question answering benchmarks cannot be executed on current versions of
their reference dataset. There is a consequently a clear need to curate question
answering benchmarks periodically. However, the manual alteration of question
answering benchmarks is often error-prone. We alleviate this problem by presenting
QUANT, a novel framework for the creation and curation of question answering benchmarks.
QUANT sup-ports the curation of benchmarks by generating smart edit suggestions
for question-query pair and for the corresponding metadata. In addition, our framework
supports the creation of new benchmark entries by pro-viding predefined quality
checks for queries. We evaluate QUANT on 653questions obtained from QALD-1 to
QALD-8 with 10 users. Our results show that our framework generates reliable suggestions
and can reduce the curation effort for QA benchmarks by up to 91%.
author:
- first_name: Ria Hari
full_name: Gusmita, Ria Hari
id: '71039'
last_name: Gusmita
- first_name: Rricha
full_name: Jalota, Rricha
last_name: Jalota
- first_name: Daniel
full_name: Vollmers, Daniel
last_name: Vollmers
- first_name: Jan
full_name: Reineke, Jan
last_name: Reineke
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
- first_name: Ricardo
full_name: Usbeck, Ricardo
last_name: Usbeck
citation:
ama: 'Gusmita RH, Jalota R, Vollmers D, Reineke J, Ngonga Ngomo A-C, Usbeck R. QUANT
- Question Answering Benchmark Curator. In: Acosta M, Cudr{\’e}-Mauroux P, Maleshkova
M, Pellegrini T, Sack H, Sure-Vetter Y, eds. Semantic Systems. The Power of
AI and Knowledge Graphs. Springer International Publishing; 2019:343--358.
doi:10.1007/978-3-030-33220-4_25'
apa: Gusmita, R. H., Jalota, R., Vollmers, D., Reineke, J., Ngonga Ngomo, A.-C.,
& Usbeck, R. (2019). QUANT - Question Answering Benchmark Curator. In M. Acosta,
P. Cudr{\’e}-Mauroux, M. Maleshkova, T. Pellegrini, H. Sack, & Y. Sure-Vetter
(Eds.), Semantic Systems. The Power of AI and Knowledge Graphs (pp. 343--358).
Springer International Publishing. https://doi.org/10.1007/978-3-030-33220-4_25
bibtex: '@inbook{Gusmita_Jalota_Vollmers_Reineke_Ngonga Ngomo_Usbeck_2019, place={Cham},
title={QUANT - Question Answering Benchmark Curator}, DOI={10.1007/978-3-030-33220-4_25},
booktitle={Semantic Systems. The Power of AI and Knowledge Graphs}, publisher={Springer
International Publishing}, author={Gusmita, Ria Hari and Jalota, Rricha and Vollmers,
Daniel and Reineke, Jan and Ngonga Ngomo, Axel-Cyrille and Usbeck, Ricardo}, editor={Acosta,
Maribel and Cudr{\’e}-Mauroux, Philippe and Maleshkova, Maria and Pellegrini,
Tassilo and Sack, Harald and Sure-Vetter, York}, year={2019}, pages={343--358}
}'
chicago: 'Gusmita, Ria Hari, Rricha Jalota, Daniel Vollmers, Jan Reineke, Axel-Cyrille
Ngonga Ngomo, and Ricardo Usbeck. “QUANT - Question Answering Benchmark Curator.”
In Semantic Systems. The Power of AI and Knowledge Graphs, edited by Maribel
Acosta, Philippe Cudr{\’e}-Mauroux, Maria Maleshkova, Tassilo Pellegrini, Harald
Sack, and York Sure-Vetter, 343--358. Cham: Springer International Publishing,
2019. https://doi.org/10.1007/978-3-030-33220-4_25.'
ieee: 'R. H. Gusmita, R. Jalota, D. Vollmers, J. Reineke, A.-C. Ngonga Ngomo, and
R. Usbeck, “QUANT - Question Answering Benchmark Curator,” in Semantic Systems.
The Power of AI and Knowledge Graphs, M. Acosta, P. Cudr{\’e}-Mauroux, M.
Maleshkova, T. Pellegrini, H. Sack, and Y. Sure-Vetter, Eds. Cham: Springer International
Publishing, 2019, pp. 343--358.'
mla: Gusmita, Ria Hari, et al. “QUANT - Question Answering Benchmark Curator.” Semantic
Systems. The Power of AI and Knowledge Graphs, edited by Maribel Acosta et
al., Springer International Publishing, 2019, pp. 343--358, doi:10.1007/978-3-030-33220-4_25.
short: 'R.H. Gusmita, R. Jalota, D. Vollmers, J. Reineke, A.-C. Ngonga Ngomo, R.
Usbeck, in: M. Acosta, P. Cudr{\’e}-Mauroux, M. Maleshkova, T. Pellegrini, H.
Sack, Y. Sure-Vetter (Eds.), Semantic Systems. The Power of AI and Knowledge Graphs,
Springer International Publishing, Cham, 2019, pp. 343--358.'
conference:
end_date: 2019-09-12
location: Karlsruhe, Germany
name: SEMANTiCS 2019
start_date: 2019-09-09
date_created: 2024-10-10T14:25:00Z
date_updated: 2024-11-19T15:34:24Z
ddc:
- '004'
doi: 10.1007/978-3-030-33220-4_25
editor:
- first_name: Maribel
full_name: Acosta, Maribel
last_name: Acosta
- first_name: Philippe
full_name: Cudr{\'e}-Mauroux, Philippe
last_name: Cudr{\'e}-Mauroux
- first_name: Maria
full_name: Maleshkova, Maria
last_name: Maleshkova
- first_name: Tassilo
full_name: Pellegrini, Tassilo
last_name: Pellegrini
- first_name: Harald
full_name: Sack, Harald
last_name: Sack
- first_name: York
full_name: Sure-Vetter, York
last_name: Sure-Vetter
file:
- access_level: closed
content_type: application/pdf
creator: gusmita
date_created: 2024-11-19T15:28:45Z
date_updated: 2024-11-19T15:28:45Z
file_id: '57244'
file_name: Gusmita_et_al-2019-Semantic_Systems._The_Power_of_AI_and_Knowledge_Graphs,_15th_International....pdf
file_size: 1179910
relation: main_file
success: 1
file_date_updated: 2024-11-19T15:28:45Z
has_accepted_license: '1'
keyword:
- Benchmark
- Question answering
- Knowledge base
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://rdcu.be/d0KJc
oa: '1'
page: 343--358
place: Cham
publication: Semantic Systems. The Power of AI and Knowledge Graphs
publication_identifier:
eisbn:
- 978-3-030-33220-4
isbn:
- 978-3-030-33219-8
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer International Publishing
status: public
title: QUANT - Question Answering Benchmark Curator
type: book_chapter
user_id: '71039'
year: '2019'
...
---
_id: '56582'
author:
- first_name: Ricardo
full_name: Usbeck, Ricardo
last_name: Usbeck
- first_name: Ria Hari
full_name: Gusmita, Ria Hari
id: '71039'
last_name: Gusmita
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
- first_name: M
full_name: Saleem, M
last_name: Saleem
citation:
ama: 'Usbeck R, Gusmita RH, Ngonga Ngomo A-C, Saleem M. 9th Challenge on Question
Answering over Linked Data (QALD-9) (invited paper). In: Joint Proceedings
of the 4th Workshop on Semantic Deep Learning (SemDeep-4) and NLIWoD4: Natural
Language Interfaces for the Web of Data {(NLIWOD-4)} and 9th Question Answering
over Linked Data Challenge {(QALD-9)} Co-Located with 17th International Semantic
Web Conference {(ISWC} 2018), Monterey, California, United States of America,
October 8th - 9th, 2018. ; 2018:58–64.'
apa: 'Usbeck, R., Gusmita, R. H., Ngonga Ngomo, A.-C., & Saleem, M. (2018).
9th Challenge on Question Answering over Linked Data (QALD-9) (invited paper).
Joint Proceedings of the 4th Workshop on Semantic Deep Learning (SemDeep-4)
and NLIWoD4: Natural Language Interfaces for the Web of Data {(NLIWOD-4)} and
9th Question Answering over Linked Data Challenge {(QALD-9)} Co-Located with 17th
International Semantic Web Conference {(ISWC} 2018), Monterey, California, United
States of America, October 8th - 9th, 2018., 58–64.'
bibtex: '@inproceedings{Usbeck_Gusmita_Ngonga Ngomo_Saleem_2018, title={9th Challenge
on Question Answering over Linked Data (QALD-9) (invited paper)}, booktitle={Joint
proceedings of the 4th Workshop on Semantic Deep Learning (SemDeep-4) and NLIWoD4:
Natural Language Interfaces for the Web of Data {(NLIWOD-4)} and 9th Question
Answering over Linked Data challenge {(QALD-9)} co-located with 17th International
Semantic Web Conference {(ISWC} 2018), Monterey, California, United States of
America, October 8th - 9th, 2018.}, author={Usbeck, Ricardo and Gusmita, Ria Hari
and Ngonga Ngomo, Axel-Cyrille and Saleem, M}, year={2018}, pages={58–64} }'
chicago: 'Usbeck, Ricardo, Ria Hari Gusmita, Axel-Cyrille Ngonga Ngomo, and M Saleem.
“9th Challenge on Question Answering over Linked Data (QALD-9) (Invited Paper).”
In Joint Proceedings of the 4th Workshop on Semantic Deep Learning (SemDeep-4)
and NLIWoD4: Natural Language Interfaces for the Web of Data {(NLIWOD-4)} and
9th Question Answering over Linked Data Challenge {(QALD-9)} Co-Located with 17th
International Semantic Web Conference {(ISWC} 2018), Monterey, California, United
States of America, October 8th - 9th, 2018., 58–64, 2018.'
ieee: 'R. Usbeck, R. H. Gusmita, A.-C. Ngonga Ngomo, and M. Saleem, “9th Challenge
on Question Answering over Linked Data (QALD-9) (invited paper),” in Joint
proceedings of the 4th Workshop on Semantic Deep Learning (SemDeep-4) and NLIWoD4:
Natural Language Interfaces for the Web of Data {(NLIWOD-4)} and 9th Question
Answering over Linked Data challenge {(QALD-9)} co-located with 17th International
Semantic Web Conference {(ISWC} 2018), Monterey, California, United States of
America, October 8th - 9th, 2018., 2018, pp. 58–64.'
mla: 'Usbeck, Ricardo, et al. “9th Challenge on Question Answering over Linked Data
(QALD-9) (Invited Paper).” Joint Proceedings of the 4th Workshop on Semantic
Deep Learning (SemDeep-4) and NLIWoD4: Natural Language Interfaces for the Web
of Data {(NLIWOD-4)} and 9th Question Answering over Linked Data Challenge {(QALD-9)}
Co-Located with 17th International Semantic Web Conference {(ISWC} 2018), Monterey,
California, United States of America, October 8th - 9th, 2018., 2018, pp.
58–64.'
short: 'R. Usbeck, R.H. Gusmita, A.-C. Ngonga Ngomo, M. Saleem, in: Joint Proceedings
of the 4th Workshop on Semantic Deep Learning (SemDeep-4) and NLIWoD4: Natural
Language Interfaces for the Web of Data {(NLIWOD-4)} and 9th Question Answering
over Linked Data Challenge {(QALD-9)} Co-Located with 17th International Semantic
Web Conference {(ISWC} 2018), Monterey, California, United States of America,
October 8th - 9th, 2018., 2018, pp. 58–64.'
date_created: 2024-10-10T14:31:09Z
date_updated: 2024-11-19T15:39:19Z
ddc:
- '004'
file:
- access_level: closed
content_type: application/pdf
creator: gusmita
date_created: 2024-11-19T15:38:56Z
date_updated: 2024-11-19T15:38:56Z
file_id: '57245'
file_name: paper-06.pdf
file_size: 408385
relation: main_file
success: 1
file_date_updated: 2024-11-19T15:38:56Z
has_accepted_license: '1'
keyword:
- 2018 dice diesel group_aksw limbo limboproject ngonga qamel simba solide usbeck
language:
- iso: eng
main_file_link:
- url: https://ceur-ws.org/Vol-2241/paper-06.pdf
page: 58–64
publication: 'Joint proceedings of the 4th Workshop on Semantic Deep Learning (SemDeep-4)
and NLIWoD4: Natural Language Interfaces for the Web of Data {(NLIWOD-4)} and 9th
Question Answering over Linked Data challenge {(QALD-9)} co-located with 17th International
Semantic Web Conference {(ISWC} 2018), Monterey, California, United States of America,
October 8th - 9th, 2018.'
status: public
title: 9th Challenge on Question Answering over Linked Data (QALD-9) (invited paper)
type: conference
user_id: '71039'
year: '2018'
...