arxiv: 2605.05931 · v1 · submitted 2026-05-07 · 💻 cs.AI

Recognition: unknown

In Data or Invisible: Toward a Better Digital Representation of Low-Resource Languages with Knowledge Graphs

Ndeye-Emilie Mbengue (WIMMICS)

Authors on Pith no claims yet

Pith reviewed 2026-05-08 10:54 UTC · model grok-4.3

classification 💻 cs.AI

keywords low-resource languagesknowledge graphslinked open datacross-lingual transferanalogical reasoninglanguage coverageKG completionLOD

0 comments

The pith

Linguistic proximity and analogical reasoning can improve multilingual knowledge graph completion for low-resource languages.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper highlights how digital technologies are increasing the data divide for low-resource languages in linked open data. It measures the imbalance by examining Wikipedia article counts and language-tagged entities in DBpedia, BabelNet, and Wikidata. The proposal then outlines plans to test cross-lingual transfer methods that use language similarity and existing alignments to select candidates for knowledge graph completion. It also suggests exploring analogical reasoning to find new correspondences based on linguistic (dis)similarities. If successful, this would allow more languages to participate in open data ecosystems.

Core claim

Analyzing language distribution across major LOD knowledge graphs reveals underrepresentation of low-resource languages, and cross-lingual transfer strategies leveraging linguistic proximity and analogical reasoning can be used to complete these graphs and increase language coverage.

What carries the argument

Cross-lingual transfer candidate selection using linguistic proximity, curated alignments, and analogical reasoning to identify correspondences between languages for knowledge graph completion.

Load-bearing premise

Linguistic proximity and analogical reasoning will lead to better cross-lingual transfers that improve knowledge graph completion and language coverage in LOD.

What would settle it

If the planned experiments demonstrate that proximity-based and analogy-based candidate selection does not yield higher completion accuracy or broader language coverage compared to standard methods, the proposed benefits would not hold.

Figures

Figures reproduced from arXiv: 2605.05931 by Ndeye-Emilie Mbengue (WIMMICS).

**Figure 1.** Figure 1: Language coverage (log-log scale) in BabelNet, Wikidata, and DBpe view at source ↗

read the original abstract

Emerging digital technologies are exacerbating the existing divide in Open Access Data (OAD) between high-and low-resource languages, excluding many communities from participating in the global digital transformation. In this PhD proposal, we aim to address this gap, focusing on the language coverage of Linked Open Data knowledge graphs (LOD KGs). First, we identify key variables that characterize language distribution in LOD, including the number of Wikipedia articles per language edition and the number of language-tagged entities in LOD KGs. These variables are analyzed across three major multilingual LOD KGs, DBpedia, BabelNet, and Wikidata, providing insights into the representation and distribution of languages within LOD. Building on this analysis, we intend to study the impact of cross-lingual transfer candidate selection on the task of multilingual KG completion. In particular, we plan to investigate strategies based on linguistic proximity and the availability of curated annotated alignments between languages. Language proximity also motivates us to explore the benefits of analogical reasoning that relies on (dis)similarities and has not yet been investigated to identify correspondences across languages to improve KG completion performance and enhance language coverage in LOD.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This PhD proposal maps language under-representation in LOD KGs and plans cross-lingual experiments but has no results yet.

read the letter

This PhD proposal sets out to document how low-resource languages are underrepresented in major linked open data knowledge graphs and then to test ways to fill those gaps using cross-lingual techniques. It starts by defining variables such as the number of Wikipedia articles per language and the count of language-tagged entities in DBpedia, BabelNet, and Wikidata. From there it plans to look at how cross-lingual transfer candidate selection affects multilingual KG completion, with special attention to linguistic proximity and curated alignments. It also flags analogical reasoning based on similarities and dissimilarities as a promising but unexplored route for finding correspondences across languages. The work does a good job laying out the practical problem. Low-resource languages are indeed missing from these resources, and tying that to digital inclusion is a reasonable motivation. The choice of three well-known KGs gives a concrete scope, and the suggested methods draw from established ideas in transfer learning and analogy without claiming they are brand new. That keeps the proposal grounded. The soft spot is obvious and central: this is entirely a plan. There are no measurements of current language distribution, no pilot experiments on transfer, and no evaluation framework described in any detail. Without those pieces it is impossible to tell whether the proposed strategies will deliver measurable gains in coverage or performance. The assumption that proximity and analogy will help is plausible on paper but remains unexamined. A reader interested in multilingual knowledge representation or in efforts to make AI tools more inclusive would find this useful as a starting point for discussion or collaboration. It shows a clear line of thinking from problem identification to potential experiments. I would not recommend sending this to peer review in its current form. A journal or conference would expect at least some preliminary data or a more developed method section. It could work as a workshop paper or as part of a PhD progress report, where the focus is on the research agenda rather than finished findings.

Referee Report

1 major / 2 minor

Summary. This manuscript is a PhD proposal that seeks to improve the representation of low-resource languages in Linked Open Data (LOD) knowledge graphs. It begins by identifying key variables characterizing language distribution, such as the number of Wikipedia articles per language and language-tagged entities, and states that these variables are analyzed across DBpedia, BabelNet, and Wikidata. The proposal then outlines plans to investigate the impact of cross-lingual transfer candidate selection on multilingual KG completion, with strategies based on linguistic proximity, curated annotated alignments, and analogical reasoning relying on (dis)similarities to identify correspondences across languages and enhance language coverage in LOD.

Significance. If the planned experiments are executed and confirm measurable improvements, the work could advance methods for multilingual KG completion and help mitigate the digital divide for low-resource languages in LOD. The emphasis on analogical reasoning based on linguistic (dis)similarities represents a novel angle not yet investigated in this context, and the structured identification of variables for language distribution provides a clear foundation. The proposal's value is prospective, as it identifies research gaps and a logical sequence of steps without presenting any completed analysis or results.

major comments (1)

Abstract: The statement that the identified variables 'are analyzed across three major multilingual LOD KGs, DBpedia, BabelNet, and Wikidata, providing insights into the representation and distribution of languages within LOD' is not accompanied by any data, tables, quantitative findings, or even preliminary results. Since the subsequent plans for cross-lingual transfer explicitly build on this analysis, the absence of these insights is load-bearing for assessing the proposal's foundation and feasibility.

minor comments (2)

The manuscript would benefit from an initial definition or expansion of the acronym 'LOD' on first use for readers outside the immediate subfield.
Adding a small number of key references to existing work on cross-lingual KG completion or analogical reasoning in knowledge graphs would better situate the proposed novelty.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the detailed review of our PhD proposal. The single major comment is addressed point-by-point below. We agree that the abstract wording requires clarification to accurately reflect the prospective nature of the work.

read point-by-point responses

Referee: [—] Abstract: The statement that the identified variables 'are analyzed across three major multilingual LOD KGs, DBpedia, BabelNet, and Wikidata, providing insights into the representation and distribution of languages within LOD' is not accompanied by any data, tables, quantitative findings, or even preliminary results. Since the subsequent plans for cross-lingual transfer explicitly build on this analysis, the absence of these insights is load-bearing for assessing the proposal's foundation and feasibility.

Authors: We acknowledge that the abstract's use of present tense ('are analyzed... providing insights') can be read as implying completed analysis and results, which is not the case. This is a PhD proposal that outlines a research plan; the identification and analysis of language-distribution variables across DBpedia, BabelNet, and Wikidata constitute the first stage of the proposed work, with no empirical findings yet available. The subsequent cross-lingual transfer experiments are explicitly conditioned on completing that analysis. To resolve the ambiguity, we will revise the abstract to future tense (e.g., 'will be analyzed... to provide insights') and add a brief sentence clarifying the sequential structure of the proposal. This change directly addresses the concern that the foundation and feasibility cannot be assessed without the insights, while preserving the logical flow from distribution analysis to cross-lingual methods. revision: yes

Circularity Check

0 steps flagged

No significant circularity in this PhD proposal

full rationale

This document is a PhD proposal that identifies variables characterizing language distribution in LOD KGs (Wikipedia articles per language, language-tagged entities) and analyzes them across DBpedia, BabelNet, and Wikidata. It then outlines intended future investigations into cross-lingual transfer candidate selection, linguistic proximity, curated alignments, and analogical reasoning for multilingual KG completion. No equations, derivations, fitted parameters, predictions, completed experiments, or quantitative results are present. No self-citations, ansatzes, or uniqueness claims are invoked in a load-bearing manner for any result, as the text consists entirely of planned work without any claimed outputs or reductions to inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The proposal rests on the domain assumption that low-resource languages are underrepresented in current LOD graphs and that linguistic proximity can be leveraged for transfer; no free parameters, new entities, or ad-hoc axioms are introduced.

axioms (1)

domain assumption Low-resource languages are underrepresented in LOD KGs such as DBpedia, BabelNet, and Wikidata
This premise motivates the entire proposal and is stated in the opening sentences of the abstract.

pith-pipeline@v0.9.0 · 5502 in / 1160 out tokens · 34197 ms · 2026-05-08T10:54:07.868386+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

46 extracted references · 31 canonical work pages · 2 internal anchors

[1]

findings-acl.15/

ODDA: An OODA-driven diverse data augmentation framework for low- resource relation extraction - ACL anthology,https://aclanthology.org/2025. findings-acl.15/

2025
[2]

Pivoted low resource multilingual translation with NER optimization | ACM transactions on asian and low-resource language information processing,https: //dl.acm.org/doi/10.1145/3727876

work page doi:10.1145/3727876
[3]

Massively multilingual neural machine translation

Aharoni, R., Johnson, M., Firat, O.: Massively multilingual neural machine trans- lation. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). pp. 3874–3884. Association for Computatio...

work page doi:10.18653/v1/n19-1388 2019
[4]

Engineering Applications of Artificial Intelligence 139, 109660 (2025)

Akhtar, M.U., Liu, J., Xie, Z., Cui, X., Liu, X., Huang, B.: Multilingual en- tity alignment by abductive knowledge reasoning on multiple knowledge graphs. Engineering Applications of Artificial Intelligence 139, 109660 (2025). https: //doi.org/https://doi.org/10.1016/j.engappai.2024.109660, https://www. sciencedirect.com/science/article/pii/S0952197624018189

work page doi:10.1016/j.engappai.2024.109660 2025
[5]

IEEE Trans

Ali, M., Berrendorf, M., Hoyt, C.T., Vermue, L., Galkin, M., Sharifzadeh, S., Fis- cher, A., Tresp, V., Lehmann, J.: Bringing light into the dark: A large-scale eval- uation of knowledge graph embedding models under a unified framework. IEEE Trans. Pattern Anal. Mach. Intell.44(12), 8825–8845 (2022).https://doi.org/ 10.1109/TPAMI.2021.3124805

work page doi:10.1109/tpami.2021.3124805 2022
[6]

ArXivabs/2111.05147 (2021), https://api.semanticscholar.org/CorpusID:243860875

Alsaidi, S., Decker, A., Marquer, E., Murena, P.A., Couceiro, M.: Tackling morpho- logical analogies using deep learning - extended version. ArXivabs/2111.05147 (2021), https://api.semanticscholar.org/CorpusID:243860875

work page arXiv 2021
[7]

In: Calzolari, N., Kan, M.Y., Hoste, V., Lenci, A., Sakti, S., Xue, N

Anil, A., Gutierrez-Basulto, V., Ibanez-Garcia, Y., Schockaert, S.: Inductive knowl- edge graph completion with GNNs and rules: An analysis. In: Calzolari, N., Kan, M.Y., Hoste, V., Lenci, A., Sakti, S., Xue, N. (eds.) Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation(LREC-COLING2024).pp...

2024
[9]

In: Cohn, T., He, Y., Liu, Y

Chen, X., Chen, M., Fan, C., Uppunda, A., Sun, Y., Zaniolo, C.: Multilingual knowledge graph completion via ensemble knowledge transfer. In: Cohn, T., He, Y., Liu, Y. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2020, Online Event, 16-20 November 2020. Findings of ACL, vol. EMNLP 2020, pp. 3227–3238. Association for Computationa...

work page doi:10.18653/v1/2020.findings-emnlp.290 2020
[11]

Dong, B., Bu, C., Wang, Y., Zhu, Y., Wu, X.: Disentangled multi-view graph neural network for multilingual knowledge graph completion. Appl. Soft Comput.183, 113605 (2025). https://doi.org/10.1016/J.ASOC.2025.113605

work page doi:10.1016/j.asoc.2025.113605 2025
[12]

Nicolás, The bar derived category of a curved dg algebra, Journal of Pure and Applied Algebra 212 (2008) 2633–2659

Haleem, A., Javaid, M., Qadri, M.A., Suman, R.: Understanding the role of digi- tal technologies in education: A review3, 275–285.https://doi.org/10.1016/j. susoc.2022.05.004

work page doi:10.1016/j 2022
[13]

In: Han, J., Kamber, M., Pei, J

Han, J., Kamber, M., Pei, J.: 10 - cluster analysis: Basic concepts and methods. In: Han, J., Kamber, M., Pei, J. (eds.) Data Mining: Concepts and Techniques (Third Edition), pp. 443–495. The Morgan Kaufmann Series in Data Management Systems, Morgan Kaufmann, Boston, third edition edn. (2012).https://doi.org/ 10.1016/B978-0-12-381479-1.00010-1

work page doi:10.1016/b978-0-12-381479-1.00010-1 2012
[14]

https://doi.org/10

Helm, P., Bella, G., Koch, G., Giunchiglia, F.: Diversity and language technology: How techno-linguistic bias can cause epistemic injustice. https://doi.org/10. 48550/arXiv.2307.13714

work page arXiv
[15]

Synthesis Lectures on Data, Semantics, and Knowledge, Morgan & Claypool Publishers (2021)

Hogan, A., Blomqvist, E., Cochez, M., d’Amato, C., de Melo, G., Gutierrez, C., Kirrane, S., Gayo, J.E.L., Navigli, R., Neumaier, S., Ngomo, A.N., Polleres, A., Rashid, S.M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., Zim- mermann, A.: Knowledge Graphs. Synthesis Lectures on Data, Semantics, and Knowledge, Morgan & Claypool Publishers (2021). http...

2021
[16]

Smith, Edoardo M

Huang, Z., Li, Z., Jiang, H., Cao, T., Lu, H., Yin, B., Subbian, K., Sun, Y., Wang, W.: Multilingual knowledge graph completion with self-supervised adaptive graph alignment. CoRR abs/2203.14987 (2022). https://doi.org/10.48550/ARXIV. 2203.14987

work page internal anchor Pith review doi:10.48550/arxiv 2022
[17]

In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

Jarnac, L., Couceiro, M., Monnin, P.: Relevant entity selection: Knowledge graph bootstrapping via zero-shot analogical pruning. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. p. 934–944. CIKM ’23, Association for Computing Machinery, New York, NY, USA (2023). https://doi.org/10.1145/3583780.3615030

work page doi:10.1145/3583780.3615030 2023
[18]

In: Rambow, O., Wanner, L., Apidianaki, M., Al-Khalifa, H., Eugenio, B.D., Schockaert, S

Jin,Z.,Zhang,C.,Hu,Z.,Yu,J.,Ma,R.,Chen,Q.,Liao,X.,Zhang,Y.:Cycleoie:A low-resource training framework for open information extraction. In: Rambow, O., Wanner, L., Apidianaki, M., Al-Khalifa, H., Eugenio, B.D., Schockaert, S. (eds.) Proceedings of the 31st International Conference on Computational Linguistics, COLING 2025, Abu Dhabi, UAE, January 19-24, 20...

2025
[19]

Joshi, S

Joshi, P., Santy, S., Budhiraja, A., Bali, K., Choudhury, M.: The state and fate of linguistic diversity and inclusion in the NLP world. In: Jurafsky, D., Chai, J., Schluter, N., Tetreault, J.R. (eds.) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020. pp. 6282–6293. Association for ...

work page doi:10.18653/v1/2020.acl-main.560 2020
[20]

In: Proceedings of the 60th Annual Meeting of the Association for Computational Lin- guistics (Volume 1: Long Papers)

Kolluru, K., Mohammed, M., Mittal, S., Chakrabarti, S., ., M.: Alignment- augmented consistent translation for multilingual open information extraction. In: Proceedings of the 60th Annual Meeting of the Association for Computational Lin- guistics (Volume 1: Long Papers). pp. 2502–2517. Association for Computational Linguistics. https://doi.org/10.18653/v1...

work page doi:10.18653/v1/2022.acl-long.179 2022
[21]

Journal of Digi- tal Economy 3, 240–248 (2024)

Li, H., Li, Q., Xu, Z., Ye, X.: Digital technologies. Journal of Digi- tal Economy 3, 240–248 (2024). https://doi.org/https://doi.org/10.1016/ j.jdec.2025.02.001, https://www.sciencedirect.com/science/article/pii/ S2773067025000032 Toward a Better Representation of Low-Resource Languages with KGs 13

2024
[22]

In: European Conference on Symbolic and Quantitative Ap- proaches to Reasoning and Uncertainty (2019),https://api.semanticscholar

Lim, S., Prade, H., Richard, G.: Solving word analogies: A machine learn- ing perspective. In: European Conference on Symbolic and Quantitative Ap- proaches to Reasoning and Uncertainty (2019),https://api.semanticscholar. org/CorpusID:202763532

2019
[23]

In: Chiruzzo, L., Ritter, A., Wang, L

Liu, X., Hu, C., Zhang, R., Chen, J., Xu, B.: Improving data annotation for low- resource relation extraction with logical rule-augmented collaborative language models. In: Chiruzzo, L., Ritter, A., Wang, L. (eds.) Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Com- putational Linguistics: Human Language T...

2025
[24]

https://doi.org/10.2196/56747

Loo, R.T.J., Nasta, F., Macchi, M., Baudot, A., Burstein, F., Bove, R., Greve, M., Fröhlich, H., Khalid, S., Küderle, A., Moore, S.L., Storms, V., Torous, J., Glaab, E.: Recommendations for successful development and implementation of digital health technology tools27, e56747. https://doi.org/10.2196/56747

work page doi:10.2196/56747
[25]

Graph entropy guided node embedding dimension selection for graph neural networks

Luo, G., Li, J., Peng, H., Yang, C., Sun, L., Yu, P.S., He, L.: Graph en- tropy guided node embedding dimension selection for graph neural networks. ArXiv abs/2105.03178 (2021), https://api.semanticscholar.org/CorpusID: 234093064

work page arXiv 2021
[26]

https://doi.org/https://doi.org/10.1016/ j.techfore.2021.121359

Lythreatis, S., Singh, S.K., El-Kassar, A.N.: The digital divide: A review and fu- ture research agenda175, 121359. https://doi.org/https://doi.org/10.1016/ j.techfore.2021.121359

work page arXiv 2021
[27]

CoRR abs/2510.07736 (2025)

Mao, C., Gao, X., Song, R., He, S., Gao, S., Liu, K., Yu, Z.: Multilingual knowledge graph completion via efficient multilingual knowledge sharing. CoRR abs/2510.07736 (2025). https://doi.org/10.48550/ARXIV.2510.07736

work page doi:10.48550/arxiv.2510.07736 2025
[28]

An- nals of Mathematics and Artificial Intelligence93(2), 263–298 (Jun 2024).https: //doi.org/10.1007/s10472-024-09945-7

Marquer, E., Couceiro, M.: Solving morphological analogies: from retrieval to generation: Solving morphological analogies: from retrieval to generation. An- nals of Mathematics and Artificial Intelligence93(2), 263–298 (Jun 2024).https: //doi.org/10.1007/s10472-024-09945-7

work page doi:10.1007/s10472-024-09945-7 2024
[29]

Mohammadshahi, A., Vamvas, J., Sennrich, R.: Investigating multi-pivot ensem- bling with massively multilingual machine translation models.https://doi.org/ 10.48550/arXiv.2311.07439

work page doi:10.48550/arxiv.2311.07439
[30]

In: Annual Meeting of the Association for Computational Linguistics (2023), https://api.semanticscholar.org/CorpusID:258865272

Naous, T., Ryan, M.J., Xu, W.: Having beer after prayer? measuring cultural bias in large language models. In: Annual Meeting of the Association for Computational Linguistics (2023), https://api.semanticscholar.org/CorpusID:258865272

2023
[31]

Nigatu, H.H., Tonja, A.L., Rosman, B., Solorio, T., Choudhury, M.: The zeno’s paradoxof’low-resource’languages.In:Al-Onaizan,Y.,Bansal,M.,Chen,Y.(eds.) Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024, Miami, FL, USA, November 12-16, 2024. pp. 17753– 17774. Association for Computational Linguistics (2024)....

2024
[33]

Semantic Web 8(3), 489–508 (2017).https://doi.org/10.3233/ SW-160218 14 N.-E

Paulheim, H.: Knowledge graph refinement: A survey of approaches and evalua- tion methods. Semantic Web 8(3), 489–508 (2017).https://doi.org/10.3233/ SW-160218 14 N.-E. Mbengue

2017
[34]

ACM Trans

Peng, B., Zhu, Y., Liu, Y., Bo, X., Shi, H., Hong, C., Zhang, Y., Tang, S.: Graph retrieval-augmented generation: A survey. ACM Trans. Inf. Syst.44(2) (Dec 2025). https://doi.org/10.1145/3777378, https://doi.org/10.1145/3777378

work page doi:10.1145/3777378 2025
[35]

Artificial Intelligence Review pp

Peng, C., Xia, F., Naseriparsa, M., Osborne, F.: Knowledge graphs: Opportunities and challenges. Artificial Intelligence Review pp. 1 – 32 (2023),https://api. semanticscholar.org/CorpusID:257757244

2023
[36]

ArXiv abs/2511.03610 (2025), https://api.semanticscholar.org/CorpusID: 282758083

Ringwald, C., Gandon, F.L., Faron-Zucker, C., Michel, F., Akl, H.A.: A sys- tematic review of relation extraction task since the emergence of transformers. ArXiv abs/2511.03610 (2025), https://api.semanticscholar.org/CorpusID: 282758083

work page arXiv 2025
[37]

CoRR abs/2302.04790 (2023)

Singh, B., Kandru, P., Sharma, A., Varma, V.: Massively multilingual language models for cross lingual fact extraction from low resource indian languages. CoRR abs/2302.04790 (2023). https://doi.org/10.48550/ARXIV.2302.04790

work page doi:10.48550/arxiv.2302.04790 2023
[38]

In: Findings of the Association for Computational Linguistics: ACL 2023

Song, R., He, S., Gao, S., Cai, L., Liu, K., Yu, Z., Zhao, J.: Multilingual knowledge graph completion from pretrained language models with knowledge constraints. In: Findings of the Association for Computational Linguistics: ACL 2023. pp. 7709–

2023
[41]

Appworld: A controllable world of apps and people for benchmarking interactive coding agents

Soto Martinez, W., Parmentier, Y., Gardent, C.: Phylogeny-inspired soft prompts for data-to-text generation in low-resource languages. In: Park, J.C., Arase, Y., Hu, B., Lu, W., Wijaya, D., Purwarianti, A., Krisnadhi, A.A. (eds.) Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacifi...

work page doi:10.18653/v1/ 2023
[42]

In: Proceedings of the 61st Annual MeetingoftheAssociationforComputationalLinguistics(Volume1:LongPapers)

Tang, R., Zhao, Y., Zong, C., Zhou, Y.: Multilingual knowledge graph completion with language-sensitive multi-graph attention. In: Proceedings of the 61st Annual MeetingoftheAssociationforComputationalLinguistics(Volume1:LongPapers). pp. 10508–10519. Association for Computational Linguistics.https://doi.org/ 10.18653/v1/2023.acl-long.586

work page doi:10.18653/v1/2023.acl-long.586 2023
[43]

Bahri, H

Tong, V., Nguyen, D.Q., Huynh, T.T., Nguyen, T.T., Nguyen, Q.V.H., Niepert, M.: Joint multilingual knowledge graph completion and alignment. In: Findings of the Association for Computational Linguistics: EMNLP 2022. pp. 4646–4658. Association for Computational Linguistics.https://doi.org/10.18653/v1/2022. findings-emnlp.341

work page doi:10.18653/v1/2022 2022
[44]

In: Artificial Intelligence, Soft Computing and Applications

Vijayan, K., Anand, O.: Language-agnostic text processing for information ex- traction. In: Artificial Intelligence, Soft Computing and Applications. pp. 129–
[45]

https: //doi.org/10.5121/csit.2022.122310

Academy and Industry Research Collaboration Center (AIRCC). https: //doi.org/10.5121/csit.2022.122310

work page doi:10.5121/csit.2022.122310 2022
[46]

Viksna, R., Skadina, I., Skadins, R., Vasiljevs, A., Rozis, R.: Assessing multilin- guality of publicly accessible websites. In: International Conference on Language Resources and Evaluation (2022),https://api.semanticscholar.org/CorpusID: 250164111 Toward a Better Representation of Low-Resource Languages with KGs 15

2022
[47]

https://doi.org/10.1016/j.dim.2025

Wang, D., Anwar, M., Tang, R.: Open access data policies and technologies: In- troduction to special issue9(1), 100093. https://doi.org/10.1016/j.dim.2025. 100093

work page doi:10.1016/j.dim.2025 2025
[48]

Emerald Publishing Limited (2022)

Wang, D., Richards, D., Bilgin, A., Chen, C.: The development of open government data: connecting supply and demand through portals. Emerald Publishing Limited (2022). https://doi.org/10.1108/9781802623154

work page doi:10.1108/9781802623154 2022
[49]

2004 International Symposium on Technology and Society (IEEE Cat

Wolk, R.M.: The effects of english language dominance of the internet and the digital divide. 2004 International Symposium on Technology and Society (IEEE Cat. No.04CH37548) pp. 174–178 (2004), https://api.semanticscholar.org/ CorpusID:6241691

2004
[50]

https://doi.org/10.1016/j.knosys.2023

Zhang, W., Şerban, O., Sun, J., Guo, Y.: Conflict-aware multilingual knowl- edge graph completion281, 111070. https://doi.org/10.1016/j.knosys.2023. 111070

work page doi:10.1016/j.knosys.2023 2023
[51]

Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Research

Zhong, T., Yang, Z., Liu, Z., Zhang, R., Liu, Y., Sun, H., Pan, Y., Li, Y., Zhou, Y., Jiang, H., Chen, J., Liu, T.: Opportunities and challenges of large language models for low-resource languages in humanities research.https://doi.org/10. 48550/arXiv.2412.04497

work page internal anchor Pith review Pith/arXiv arXiv